Paper Digest: KDD 2025 Papers & Highlights
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TABLE 1: Paper Digest: KDD 2025 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | ResMoE: Space-efficient Compression of Mixture of Experts LLMs Via Residual Restoration Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The sparse structure, while allowing constant time costs, results in space inefficiency: we still need to load all the model parameters during inference. We introduce ResMoE, an innovative MoE approximation framework that utilizes Wasserstein barycenter to extract a common expert (barycenter expert) and approximate the residuals between this barycenter expert and the original ones. |
Mengting Ai; Tianxin Wei; Yifan Chen; Zhichen Zeng; Ritchie Zhao; Girish Varatkar; Bita Darvish Rouhani; Xianfeng Tang; Hanghang Tong; Jingrui He; |
| 2 | Hypergraph Motif Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For this reason, predicting h-motifs can be highly beneficial in different fields. In this paper, we aim to advance our understanding of such complex high-order dynamics by introducing and formalizing the problem of h-motifs prediction. |
Alessia Antelmi; Gennaro Cordasco; Daniele De Vinco; Valerio Di Pasquale; Mirko Polato; Carmine Spagnuolo; |
| 3 | Chainlet Orbits: Topological Address Embedding for Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional analysis methods rely on simple heuristics and extensive data gathering, while more advanced Graph Neural Networks encounter challenges such as scalability, poor interpretability, and label scarcity in massive blockchain transaction networks.To overcome existing techniques’ computational and interpretability limitations, we introduce a topological approach, Chainlet Orbits, which embeds blockchain addresses by leveraging their topological characteristics in temporal transactions. |
Poupak Azad; Baris Coskunuzer; Murat Kantarcioglu; Cuneyt G. Akcora; |
| 4 | Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this is quite challenging due to the requirement of retaining the distinctiveness of each label while fully harnessing the correlation between labels simultaneously. To address these issues, in this paper, we propose a Correlation-aware Graph Convolutional Network (CorGCN) for multi-label node classification. |
Yuanchen Bei; Weizhi Chen; Hao Chen; Sheng Zhou; Carl Yang; Jiapei Fan; Longtao Huang; Jiajun Bu; |
| 5 | Fast and Effective GNN Training Through Sequences of Random Path Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. |
Francesco Bonchi; Claudio Gentile; Francesco Paolo Nerini; Andr\'{e} Panisson; Fabio Vitale; |
| 6 | How to Use Graph Data in The Wild to Help Graph Anomaly Detection? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, when the available data is insufficient, capturing the normal distribution accurately and comprehensively becomes difficult. To overcome this limitation, we propose to utilize external graph data (i.e., graph data in the wild) to help anomaly detection tasks. |
Yuxuan Cao; Jiarong Xu; Chen Zhao; Jiaan Wang; Carl Yang; Chunping Wang; Yang Yang; |
| 7 | Safe Online Bid Optimization with Return on Investment and Budget Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We provide the GCB algorithm that guarantees sublinear regret at the cost of a linear number of constraint violations and GCBsafe that guarantees w.h.p.a constant upper bound on the number of constraint violations at the cost of a linear regret. |
Matteo Castiglioni; Alessandro Nuara; Giulia Romano; Giorgio Spadaro; Francesco Trov\`{o}; Nicola Gatti; |
| 8 | Probabilistic Hypergraph Recurrent Neural Networks for Time-series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel model, Probabilistic Hypergraph Recurrent Neural Networks (PHRNN), which leverages node-hyperedge dynamics for accurate time-series forecasting. |
Hongjie Chen; Ryan A. Rossi; Sungchul Kim; Kanak Mahadik; Hoda Eldardiry; |
| 9 | NodeImport: Imbalanced Node Classification with Node Importance Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel approach to class-imbalanced node classification by utilizing a balanced meta-set for importance measurement, where a training node is considered significant if it enhances model performance under an unbiased setting. |
Nan Chen; Zemin Liu; Bryan Hooi; Bingsheng He; Jun Hu; Jia Chen; |
| 10 | Advancing Confidence Calibration and Quantification in Medication Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce two innovative methodologies tailored to the unique challenges of MR scenarios: 1) A discernible binning-based calibration method with theoretical guarantees for the confidence of individual medication. |
Qianyu Chen; Xin Li; Yujie Fang; Mingzhong Wang; |
| 11 | Locally Balancing Signed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, this paper shifts the focus to local balance, where a vertex is deemed balanced when the triangles (length-three cycles) it participates in are positive, reflecting more immediate relationships. Building on this, we introduce the Locally Balancing Signed Graph (LBS) problem, which aims to maximize the number of locally balanced vertices through graph modification. |
Weizhe Chen; Wentao Li; Min Gao; Dong Wen; Maolin Cai; Wei Wang; |
| 12 | Scalable Link Recommendation for Influence Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Putting it together, we develop a scalable algorithm for the IMA problem, namely ScaLIM. |
Xiaolong Chen; Jing Tang; |
| 13 | Seeing The Unseen in Micro-Video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing methodologies predominantly assume complete modalities during multimodal learning, this assumption often fails to hold in practical scenarios due to various constraints, such as privacy concerns or data integrity issues. To address this limitation, we propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in MVPP. |
Zhangtao Cheng; Jian Lang; Ting Zhong; Fan Zhou; |
| 14 | Modeling Time-evolving Causality Over Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How efficiently can we reveal dynamical patterns that allow us to forecast future values? In this paper, we present a novel streaming method, ModePlait, which is designed for modeling such causal relationships (i.e., time-evolving causality) in multivariate co-evolving data streams and forecasting their future values. |
Naoki Chihara; Yasuko Matsubara; Ren Fujiwara; Yasushi Sakurai; |
| 15 | CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider the problem of safe policy improvement, where one only adopts a new policy if it is deemed to be better than the specified baseline with at least a pre-specified probability. |
Brian Cho; Ana-Roxana Pop; Kyra Gan; Sam Corbett-Davies; Israel Nir; Ariel Evnine; Nathan Kallus; |
| 16 | Mixing Time Matters: Accelerating Effective Resistance Estimation Via Bidirectional Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, given any nodes s and t in an undirected graph G, we aim to efficiently estimate the ER value R(s,t) between nodes s and t, ensuring a small absolute error ? |
Guanyu Cui; Hanzhi Wang; Zhewei Wei; |
| 17 | Fair Set Cover Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we develop multiple versions of fair set cover, study their hardness, and devise efficient approximation algorithms for each variant. |
Mohsen Dehghankar; Rahul Raychaudhury; Stavros Sintos; Abolfazl Asudeh; |
| 18 | Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these similarities oversimplify complex human mobility patterns (a.k.a. ”Datasaurus”), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. |
Bangchao Deng; Xin Jing; Tianyue Yang; Bingqing Qu; Dingqi Yang; Philippe Cudre-Mauroux; |
| 19 | Large-Scale Spectral Graph Neural Networks Via Laplacian Sparsification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works have attempted to scale up spectral GNNs by eliminating the linear layers on the input node features, a change that can disrupt end-to-end training, potentially impact performance, and become impractical with high-dimensional input features. To address the above challenges, we propose ”Spectral Graph Neural Networks with Laplacian Sparsification (SGNN-LS)”, a novel graph spectral sparsification method to approximate the propagation patterns of spectral GNNs. |
Haipeng Ding; Zhewei Wei; Yuhang Ye; |
| 20 | Personalized Language Model Learning on Text Data Without User Identifiers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose to let each mobile device maintain a user-specific distribution to dynamically generate user embeddings, thereby breaking the one-to-one mapping between an embedding and a specific user. |
Yucheng Ding; Yangwenjian Tan; Xiangyu Liu; Chaoyue Niu; Fandong Meng; Jie Zhou; Ning Liu; Fan Wu; Guihai Chen; |
| 21 | Stabilizing Modality Gap \& Lowering Gradient Norms Improve Zero-Shot Adversarial Robustness of VLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe for CLIP fine-tuning that zero-shot adversarial robustness improves when we (i) stabilize the modality gap (a phenomenon where image and text features occupy different feature space regions) and (ii) lower/stabilize gradient norms. |
Junhao Dong; Piotr Koniusz; Xinghua Qu; Yew-Soon Ong; |
| 22 | Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation becomes especially pronounced when dealing with event data that is associated with multi-dimensional or high-dimensional marks such as texts or images. To address this challenge, this study proposes a novel event-generation framework for modeling point processes with high-dimensional marks. |
Zheng Dong; Zekai Fan; Shixiang Zhu; |
| 23 | Bi-Dynamic Graph ODE for Opinion Evolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous studies have often modeled the opinion dynamics as a discrete and homogeneous process, neglecting its continuous and complex nature. To fill this gap, we propose a Bi-Dynamics Graph Ordinary Differential Equation (BDG-ODE) framework, which models complex opinion dynamics as the result of two dynamical processes: the evolution of positive and negative opinions. |
Bowen Duan; Henggang Deng; Jinghua Piao; Huandong Wang; Yue Wang; |
| 24 | The K-Trine Cohesive Subgraph and Its Efficient Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce and study a novel cohesive subgraph model, named k-trine, to address the defects in the classical k-core and k-truss models. |
Jinyu Duan; Haicheng Guo; Fan Zhang; Kai Wang; Zhengping Qian; Zhihong Tian; |
| 25 | Dynamic Localisation of Spatial-Temporal Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. |
Wenying Duan; Shujun Guo; Zimu Zhou; Wei Huang; Hong Rao; Xiaoxi He; |
| 26 | IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the former would fail for the unknown shift beyond simple statistics, while the latter has limited compatibility on different forecasting models. To overcome these problems, we first propose a decoupled formulation for time series forecasting, with no reliance on fixed statistics and no restriction on forecasting architectures. |
Wei Fan; Shun Zheng; Pengyang Wang; Rui Xie; Kun Yi; Qi Zhang; Jiang Bian; Yanjie Fu; |
| 27 | Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: From the spatial data management perspective, we present a novel Transformer framework called PatchSTG to efficiently and dynamically model spatial dependencies for large-scale traffic forecasting with interpretability and fidelity. |
Yuchen Fang; Yuxuan Liang; Bo Hui; Zezhi Shao; Liwei Deng; Xu Liu; Xinke Jiang; Kai Zheng; |
| 28 | Forward Once for All: Structural Parameterized Adaptation for Efficient Cloud-coordinated On-device Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, imposing a uniform model structure across heterogeneous devices may result in risking inefficacy on less capable devices or sub-optimal performance on those with sufficient capabilities. In response to these gaps, our paper introduces Forward-OFA, a novel approach for the dynamic construction of device-specific networks (both structure and parameters). |
Kairui Fu; Zheqi Lv; Shengyu Zhang; Fan Wu; Kun Kuang; |
| 29 | FABind+: Enhancing Molecular Docking Through Improved Pocket Prediction and Pose Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While traditional techniques rely on extensive sampling and simulation governed by physical principles, deep learning has emerged as a promising alternative, offering improvements in both accuracy and efficiency. Building upon the foundational work of FABind, a model focused on speed and accuracy, we introduce FABind+, an enhanced iteration that significantly elevates the performance of its predecessor. |
Kaiyuan Gao; Qizhi Pei; Gongbo Zhang; Jinhua Zhu; Kun He; Lijun Wu; |
| 30 | Wedjat: Detecting Sophisticated Evasion Attacks Via Real-time Causal Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Robust detection against evasion attacks remains an open problem. To the end, we develop Wedjat, which utilizes a causal network to model benign packet interactions among relevant flows, such that it recognizes abnormal causality that represents malicious traffic and disrupted causality incurred by evasion attacks. |
Li Gao; Chuanpu Fu; Xinhao Deng; Ke Xu; Qi Li; |
| 31 | Denoising Programming Knowledge Tracing with A Code Graph-based Tuning Adaptor Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This practical challenge significantly limits model performance and application. To address this issue, we propose Coda, a Code graph-based tuning adaptor designed to enhance existing PKT models by identifying and mitigating the impact of noise. |
Weibo Gao; Qi Liu; Rui Li; Yuze Zhao; Hao Wang; Linan Yue; Fangzhou Yao; Zheng Zhang; |
| 32 | Detecting Interpretable Subgroup Drifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed approach provides a valuable tool for monitoring model performance in dynamic real-world applications, offering insights into the evolving nature of data and ultimately contributing to more robust and adaptive models. |
Flavio Giobergia; Eliana Pastor; Luca de Alfaro; Elena Baralis; |
| 33 | Benchmarking Fraud Detectors on Private Graph Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the novel problem of benchmarking fraud detectors on private graph-structured data. |
Alexander Goldberg; Giulia Fanti; Nihar Shah; Steven Wu; |
| 34 | Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing TTA methods, originally designed for visual tasks, may not effectively handle the complex temporal dynamics of real-world time series data, resulting in suboptimal adaptation performance. To address this gap, we propose Augmented Contrastive Clustering with Uncertainty-aware Prototyping (ACCUP), a straightforward yet effective TTA method for time series data. |
Peiliang Gong; Mohamed Ragab; Min Wu; Zhenghua Chen; Yongyi Su; Xiaoli Li; Daoqiang Zhang; |
| 35 | Revisiting Cognition in Neural Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we revisit essence of cognition in Educational Psychology Theory and propose a novel Cognition-aware Cognitive Diagnosis (CCD) model, where we first introduce the Cognition factor as a bridge into the long-standing three-basic-factors (Student, Exercise, Knowledge concept) paradigm. |
Hengnian Gu; Guoqian Luo; Xiaoxiao Dong; Shulin Li; Dongdai Zhou; |
| 36 | Adaptive Domain Inference Attack with Concept Hierarchy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Can removing the domain information from model APIs protect models from these attacks? This paper studies this critical problem. |
Yuechun Gu; Jiajie He; Keke Chen; |
| 37 | Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose D3Rec (Disentangled Diffusion model for Diversified Recommendation), an end-to-end method that controls the accuracy-diversity trade-off at inference. |
Gwangseok Han; Wonbin Kweon; Minsoo Kim; Hwanjo Yu; |
| 38 | Lorentzian Residual Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current methods for constructing hyperbolic residual networks suffer from limitations such as increased model complexity, numerical instability, and errors due to multiple mappings to and from the tangent space. To address these limitations, we introduce LResNet, a novel Lorentzian residual neural network based on the weighted Lorentzian centroid in the Lorentz model of hyperbolic geometry. |
Neil He; Menglin Yang; Rex Ying; |
| 39 | UniGraph: Learning A Unified Cross-Domain Foundation Model for Text-Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation stems from the inherent complexity and diversity of graph structures, along with the different feature and label spaces specific to graph data. In this paper, we recognize text as an effective unifying medium and employ Text-Attributed Graphs (TAGs) to leverage this potential. |
Yufei He; Yuan Sui; Xiaoxin He; Bryan Hooi; |
| 40 | D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose D-Tracker, a method for continuously capturing time-varying temporal patterns within social activity tensor data streams and forecasting future activities. |
Shingo Higashiguchi; Yasuko Matsubara; Koki Kawabata; Taichi Murayama; Yasushi Sakurai; |
| 41 | InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we emphasize the necessity of mitigating bias in pre-trained diffusion models without relying on auxiliary bias annotations. To tackle this problem, we propose a framework, InvDiff, which aims to learn invariant semantic information for diffusion guidance. |
Min Hou; Yueying Wu; Chang Xu; Yu-Hao Huang; Chenxi Bai; Le Wu; Jiang Bian; |
| 42 | TransPlace: Transferable Circuit Global Placement Via Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study presents TransPlace, a global placement framework that learns to place millions of mixed-size cells in continuous space. |
Yunbo Hou; Haoran Ye; Shuwen Yang; Yingxue Zhang; Siyuan Xu; Guojie Song; |
| 43 | AgentGen: Enhancing Planning Abilities for Large Language Model Based Agent Via Environment and Task Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address this limitation, this paper explores the automated synthesis of diverse environments and a gradual range of planning tasks, from easy to difficult. We introduce a framework, AgentGen, that leverages LLMs first to generate environments and subsequently generate planning tasks conditioned on these environments. |
Mengkang Hu; Pu Zhao; Can Xu; Qingfeng Sun; Jian-Guang Lou; Qingwei Lin; Ping Luo; Saravan Rajmohan; |
| 44 | Multi-level Matching Network for Multimodal Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: On the other hand, they lack mechanisms to capture bidirectional cross-modal interaction. To address these issues, we propose a Multi-level Matching network for Multimodal Entity Linking(M3EL). |
Zhiwei Hu; Victor Guti\'{e}rrez-Basulto; Ru Li; Jeff Z. Pan; |
| 45 | DIPS: Optimal Dynamic Index for Poisson ?ps Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses the Poisson ?ps sampling problem, a topic of significant academic interest in various domains and with practical data mining applications, such as influence maximization. |
Jinchao Huang; Sibo Wang; |
| 46 | Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce a novel Progressive Dependency representation learning solution with Uncertain risk contrasting (PDU), primarily seeking to progressively uncover multiple dependency dynamics from historical trading signals for stock ranking in addition to addressing the uncertain risks. |
Li Huang; Yanzhe Xie; Qiang Gao; Kunpeng Zhang; Guisong Liu; Xueqin Chen; |
| 47 | Path Complex Neural Networks for Sequential Process Activities Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As such, this study aims to enhance process mining from event logs by proposing a novel path-complex construction based on process mining sequential data and a path-complex-based message-passing mechanism for higher-order structural information. |
Liang Huang; Kelin Xia; Chuan-Shen Hu; |
| 48 | On The Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we mapped 1,500 HP loss landscapes of 6 representative ML models on 63 datasets across different fidelity levels, with 11M+ configurations. |
Mingyu Huang; Ke Li; |
| 49 | Partial Pre-Post Code Tree: A Memory-Efficient Tree Structure for Conjunctive Rule Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce partial pre-post code trees (P3C-trees), which are based on the idea that partial trees are iteratively constructed, and immediately converted into N-lists. |
Van Quoc Phuong Huynh; Florian Beck; Johannes F\{u}rnkranz; |
| 50 | Seeing The Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods have limitations in addressing continuous or undefined confounders, as they depend on predefined discrete values that are often impractical in complex, real-world scenarios. To overcome this challenge, we propose the Spatial-Temporal sElf-superVised confoundEr learning (STEVE) model. |
Jiahao Ji; Wentao Zhang; Jingyuan Wang; Chao Huang; |
| 51 | PipeRAG: Fast Retrieval-Augmented Generation Via Adaptive Pipeline Parallelism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce PipeRAG, a novel algorithm-system co-design approach to reduce generation latency and enhance generation quality. |
Wenqi Jiang; Shuai Zhang; Boran Han; Jie Wang; Bernie Wang; Tim Kraska; |
| 52 | Why Not Together? A Multiple-Round Recommender System for Queries and Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel approach named Multiple-round Auto Guess-and-Update System (MAGUS) that capitalizes on the synergies between both types, allowing us to leverage both query and item information to form user interests. |
Jiarui Jin; Xianyu Chen; Weinan Zhang; Yong Yu; Jun Wang; |
| 53 | On Measuring Unnoticeability of Graph Adversarial Attacks: Observations, New Measure, and Applications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: ”To address the limitations, we introduce HideNSeek, a learnable measure for graph attack noticeability. |
Hyeonsoo Jo; Hyunjin Hwang; Fanchen Bu; Soo Yong Lee; Chanyoung Park; Kijung Shin; |
| 54 | Simplicial SMOTE: Oversampling Solution to The Imbalanced Learning Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: SMOTE (Synthetic Minority Oversampling Technique) is the established geometric approach to random oversampling to balance classes in the imbalanced learning problem, followed by many extensions. Its idea is to introduce synthetic data points of the minor class, with each new point being the convex combination of an existing data point and one of its k-nearest neighbors.In this paper, by viewing SMOTE as sampling from the edges of a geometric neighborhood graph and borrowing tools from the topological data analysis, we propose a novel technique, Simplicial SMOTE, that samples from the simplices of a geometric neighborhood simplicial complex. |
Oleg Kachan; Andrey Savchenko; Gleb Gusev; |
| 55 | LH-Mix: Local Hierarchy Correlation Guided Mixup Over Hierarchical Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. |
Fanshuang Kong; Richong Zhang; Ziqiao Wang; |
| 56 | CAPER: Enhancing Career Trajectory Prediction Using Temporal Knowledge Graph and Ternary Relationship Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. |
Yeon-Chang Lee; JaeHyun Lee; Michiharu Yamashita; Dongwon Lee; Sang-Wook Kim; |
| 57 | Reasoning-Enhanced Object-Centric Learning for Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the real world, reasoning and predictive abilities play a crucial role in human perception and object tracking; in particular, these abilities are closely related to human intuitive physics. Inspired by this, we designed a novel reasoning module called the Slot-based Time-Space Transformer with Memory buffer (STATM) to enhance the model’s perception ability in complex scenes. |
Jian Li; Pu Ren; Yang Liu; Hao Sun; |
| 58 | TSINR: Capturing Temporal Continuity Via Implicit Neural Representations for Time Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the unlabeled anomaly points in training data may cause these reconstruction-based methods to learn and reconstruct anomalous data, resulting in the challenge of capturing normal patterns. In this paper, we propose a time series anomaly detection method based on implicit neural representation (INR) reconstruction, named TSINR, to address this challenge. |
Mengxuan Li; Ke Liu; Hongyang Chen; Jiajun Bu; Hongwei Wang; Haishuai Wang; |
| 59 | Diversity Optimization for Travelling Salesman Problem Via Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To discover diverse yet high-quality solutions for Multi-Solution TSP (MSTSP), we propose a novel deep reinforcement learning based neural solver, which is primarily featured by an encoder-decoder structured policy. |
Qi Li; Zhiguang Cao; Yining Ma; Yaoxin Wu; Yue-Jiao Gong; |
| 60 | MGS3: A Multi-Granularity Self-Supervised Code Search Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we introduce a novel Multi-Granularity Self-Supervised contrastive learning code Search framework (MGS3). |
Rui Li; Junfeng Kang; Qi Liu; Liyang He; Zheng Zhang; Yunhao Sha; Linbo Zhu; Zhenya Huang; |
| 61 | TSPRank: Bridging Pairwise and Listwise Methods with A Bilinear Travelling Salesman Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Conversely, deep learning based listwise methods, while aiming to optimise entire lists, require complex tuning and yield only marginal improvements over robust pairwise models. To overcome these limitations, we introduce Travelling Salesman Problem Rank (TSPRank), a hybrid pairwise-listwise ranking method. |
Weixian Waylon Li; Yftah Ziser; Yifei Xie; Shay B. Cohen; Tiejun Ma; |
| 62 | Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we explore to utilize the explicit information injection capability of DMs for user preference integration and propose a Preference-Guided Diffusion Model for CDR to cold-start users, termed as DMCDR. |
Xiaodong Li; Hengzhu Tang; Jiawei Sheng; Xinghua Zhang; Li Gao; Suqi Cheng; Dawei Yin; Tingwen Liu; |
| 63 | Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces the AMG method, a Multi-Graph neural operator approach designed for efficiently solving PDEs on Arbitrary geometries. |
Zhihao Li; Haoze Song; Di Xiao; Zhilu Lai; Wei Wang; |
| 64 | APEX2: Adaptive and Extreme Summarization for Personalized Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, when the size constraint of PKG is extremely small, the existing methods cannot distinguish which facts are more of immediate interest and guarantee the utility of the summarized PKG. To address these limitations, we propose APEX2, a highly scalable PKG summarization framework designed with robust theoretical guarantees to excel in adaptive summarization tasks with extremely small size constraints. |
Zihao Li; Dongqi Fu; Mengting Ai; Jingrui He; |
| 65 | DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel approach called DistPred for regression and forecasting tasks, which overcomes the limitations of existing methods while remaining simple and powerful. |
Daojun Liang; |
| 66 | Learnable Prompt As Pseudo-Imputation: Rethinking The Necessity of Traditional EHR Data Imputation in Downstream Clinical Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the recent advanced literature of learnable prompt in the fields of NLP and CV, in this work, we rethought the necessity of the imputation model in downstream clinical tasks, and proposed Learnable Prompt as Pseudo-Imputation (PAI) as a new training protocol to assist EHR analysis. |
Weibin Liao; Yinghao Zhu; Zhongji Zhang; Yuhang Wang; Zixiang Wang; Xu Chu; Yasha Wang; Liantao Ma; |
| 67 | Stealing Training Graphs from Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, explorations into training data leakage from trained GNNs are rather limited. Therefore, we investigate a novel problem of stealing graphs from trained GNNs. |
Minhua Lin; Enyan Dai; Junjie Xu; Jinyuan Jia; Xiang Zhang; Suhang Wang; |
| 68 | Spectral Subspace Clustering for Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these works either demand significant computational overhead for constructing the nxn self-expressive matrix, or fail to incorporate graph topology and attribute data into the subspace clustering framework effectively, and thus, compromise result quality.Motivated by this, this paper presents two effective and efficient algorithms, S2CAG M-S2CAG for SCAG computation. |
Xiaoyang Lin; Renchi Yang; Haoran Zheng; Xiangyu Ke; |
| 69 | Learning Attribute As Explicit Relation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, Transformers mainly focus on intra-sequence attention for item attributes, neglecting cross-sequence relations and user attributes. Addressing these challenges, we propose the Attribute Transformer (AttrFormer) to learn attributes as explicit relations. |
Gang Liu; Fan Yang; Yang Jiao; Alireza Bagheri Garakani; Tian Tong; Yan Gao; Meng Jiang; |
| 70 | SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing PTQ methods usually rely on various complex computation procedures and suffer from considerable performance degradation under low-bit quantization settings. To alleviate the above issues, we propose a simple and effective post-training quantization paradigm for LLMs, named SEPTQ. |
Han Liu; Haotian Gao; Xiaotong Zhang; Changya Li; Feng Zhang; Wei Wang; Fenglong Ma; Hong Yu; |
| 71 | SCode: A Spherical Code Metric Learning Approach to Continuously Monitoring Predictive Events in Networked Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the monitoring of predictive events in dynamic graphs. |
Qu Liu; Emil Zulawnik; Tingjian Ge; |
| 72 | Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation may hinder their directly practical application in various target domains, such as different subjects (e.g., Math, English and Physics) or different education platforms (e.g., ASSISTments, Junyi Academy and Khan Academy). To address this issue, this paper proposes the language representation favored zero-shot cross-domain cognitive diagnosis (LRCD). |
Shuo Liu; Zihan Zhou; Yuanhao Liu; Jing Zhang; Hong Qian; |
| 73 | Fine-tuning Multimodal Large Language Models for Product Bundling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. |
Xiaohao Liu; Jie Wu; Zhulin Tao; Yunshan Ma; Yinwei Wei; Tat-seng Chua; |
| 74 | 3DGraphX: Explaining 3D Molecular Graph Models Via Incorporating Chemical Priors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel and principled paradigm, known as 3DGraphX, for 3D molecular graph explanation. |
Xufeng Liu; Dongsheng Luo; Wenhan Gao; Yi Liu; |
| 75 | Enhancing Unsupervised Graph Few-shot Learning Via Set Functions and Optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. |
Yonghao Liu; Fausto Giunchiglia; Ximing Li; Lan Huang; Xiaoyue Feng; Renchu Guan; |
| 76 | MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Drawing inspiration from object-oriented programming principles, we recognize that object-oriented solutions is more suitable for cross-app instruction. To address these challenges, we propose a self-evolving multi-agent framework named MobileSteward which integrates multiple app-oriented StaffAgents coordinated by a centralized StewardAgent. |
Yuxuan Liu; Hongda Sun; Wei Liu; Jian Luan; Bo Du; Rui Yan; |
| 77 | A Universal Model for Human Mobility Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to unify mobility prediction to break through the limitations of task-specific models. |
Qingyue Long; Yuan Yuan; Yong Li; |
| 78 | Future Matters for Present: Towards Effective Physical Simulation Over Meshes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates the problem of learning mesh-based physical simulations, which is a crucial task with applications in fluid mechanics and aerodynamics. |
Xiao Luo; Junyu Luo; Huiyu Jiang; Hang Zhou; Zhiping Xiao; Wei Ju; Carl Yang; Ming Zhang; Yizhou Sun; |
| 79 | Fairness Without Demographics Through Learning Graph of Gradients Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show that the correlation between gradients and groups can help identify and improve group fairness. |
Yingtao Luo; Zhixun Li; Qiang Liu; Jun Zhu; |
| 80 | Online Item Cold-Start Recommendation with Popularity-Aware Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Yet, these schemes are infeasible for online recommendation on streaming data pipelines due to different training method, computational overhead and time constraints. Inspired by the above questions, we propose a model-agnostic recommendation algorithm called Popularity-Aware Meta-learning (PAM), to address the item cold-start problem under streaming data settings. |
Yunze Luo; Yuezihan Jiang; Yinjie Jiang; Gaode Chen; Jingchi Wang; Kaigui Bian; Peiyi Li; Qi Zhang; |
| 81 | Dynamic Deep Clustering of High-Dimensional Directional Data Via Hyperspherical Embeddings with Bayesian Nonparametric Mixtures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, dynamically inferring the number of clusters remains a fundamental issue in existing deep clustering methods, especially those involving complex model-selection criteria. This paper addresses these challenges by introducing a novel deep nonparametric clustering framework that employs hyperspherical latent embeddings within a Variational Autoencoder architecture, enhanced by an infinite Von Mises-Fisher Mixture Model as a dynamic prior. |
Zhiwen Luo; Wentao Fan; Manar Amayri; Nizar Bouguila; |
| 82 | Towards Controllable Hybrid Fairness in Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To achieve this objective, we propose a novel GNN framework called LibraGNN. |
Zihan Luo; Hong Huang; Jianxun Lian; Xiran Song; Hai Jin; |
| 83 | Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, its inability to capture real-time user preferences greatly limits the practical application of LLM4Rec because (i) LLMs are costly to train and infer frequently, and (ii) LLMs struggle to access real-time data (its large number of parameters poses an obstacle to deployment on devices). Fortunately, small recommendation models (SRMs) can effectively supplement these shortcomings of LLM4Rec diagrams by consuming minimal resources for frequent training and inference, and by conveniently accessing real-time data on devices.In light of this, we designed the Device-Cloud LLM-SRM Collaborative Recommendation Framework (LSC4Rec) under a device-cloud collaboration setting. |
Zheqi Lv; Tianyu Zhan; Wenjie Wang; Xinyu Lin; Shengyu Zhang; Wenqiao Zhang; Jiwei Li; Kun Kuang; Fei Wu; |
| 84 | Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a principled integration of multi-task learning using multi-output Gaussian processes (MOGP) at the local level and federated learning at the global level. |
Junliang Lyu; Yixuan Zhang; Xiaoling Lu; Feng Zhou; |
| 85 | AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AutoSTF, a decoupled automatic neural architecture search framework for cost-effective automated spatio-temporal forecasting. |
Tengfei Lyu; Weijia Zhang; Jinliang Deng; Hao Liu; |
| 86 | Diffusion-Inspired Cold Start with Sufficient Prior in Computerized Adaptive Testing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, no prior work has explored solutions for the CSIP task. In response to this gap, we propose Diffusion Cognitive States TransfeR Framework (DCSR), a novel domain transfer framework based on Diffusion Models (DMs) to address the CSIP task. |
Haiping Ma; Aoqing Xia; Changqian Wang; Hai Wang; Xingyi Zhang; |
| 87 | Achieving Nearly-Optimal Regret and Sample Complexity in Dueling Bandits with Applications in Online Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework that demonstrates the near-consistency of RM and BAI in dueling bandits by reducing the BAI in dueling bandits into a sequential noisy identification problem. |
Lanjihong Ma; Yao-Xiang Ding; Zhen-Yu Zhang; Zhi-Hua Zhou; |
| 88 | On The Support Vector Effect in DNNs: Rethinking Data Selection and Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce SVE, a max-margin-like behavior in the last layer(s) of DNNs and employ it to thoroughly scrutinize prevalent data selection and attribution methods relying on last layer gradients. |
Syed Hasan Amin Mahmood; Ming Yin; Rajiv Khanna; |
| 89 | Enhancing Black-Box Adversarial Attacks on Discrete Sequential Data Via Bilevel Bayesian Optimization in Hybrid Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it relies solely on alignment information (i.e., positional differences) within the RBF kernel, which may not fully capture the information (such as statistical, structural, and semantic information) inherent in discrete sequential data and potentially lacks the desired inductive bias necessary to approximate the target function accurately. To overcome this limitation, this paper proposes a novel bilevel Bayesian optimization approach to adaptively learn a hybrid space that better captures the similarity between discrete sequences. |
Tianxing Man; Xingchen Li; Zhaogeng Liu; Haozhen Zhang; Bin Gu; Yi Chang; |
| 90 | Attribute-Enhanced Similarity Ranking for Sparse Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we show that the reported performance of GNNs for link prediction in the balanced setting does not translate to the more realistic imbalanced setting and that simpler topology-based approaches are often better at handling sparsity. These findings motivate Gelato, a similarity-based link-prediction method that applies (1) graph learning based on node attributes to enhance a topological heuristic, (2) a ranking loss for addressing class imbalance, and (3) a negative sampling scheme that efficiently selects hard training pairs via graph partitioning. |
Joao Mattos; Zexi Huang; Mert Kosan; Ambuj Singh; Arlei Silva; |
| 91 | Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we herein introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework, to learn spatiotemporal dynamics based on limited training data. |
Yuan Mi; Pu Ren; Hongteng Xu; Hongsheng Liu; Zidong Wang; Yike Guo; Ji-Rong Wen; Hao Sun; Yang Liu; |
| 92 | Data Glitches Discovery Using Influence-based Model Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce three novel signals for detecting, characterizing, and repairing data glitches in a training set based on sample influences. |
Nikolaos Myrtakis; Ioannis Tsamardinos; Vassilis Christophides; |
| 93 | Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. |
Gyoung S. Na; Chanyoung Park; |
| 94 | Weight-Constrained Simple Path Enumeration in Weighted Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we are the first to propose a join-based framework for weighted graphs. |
Dian Ouyang; Dong Wen; Jianye Yang; Wentao Li; Xuemin Lin; |
| 95 | Distributional Prototype Learning for Out-of-distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In view of this, this paper extends the existing prototype-based learning paradigm to an infinite setting. This motivates us to design two feasible formulations for the Distributional Prototype Learning (DPL) objective, where, to avoid intractable computation and exploding parameters caused by the infinity nature, our key idea is to model an infinite number of discrete prototypes of each ID class with a class-wise continuous distribution. |
Bo Peng; Jie Lu; Yonggang Zhang; Guangquan Zhang; Zhen Fang; |
| 96 | On The Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we observe that systematic missing labels lead to missing knowledge, which is critical for modelling relevance between queries and documents. |
Jatin Prakash; Anirudh Buvanesh; Bishal Santra; Deepak Saini; Sachin Yadav; Jian Jiao; Yashoteja Prabhu; Amit Sharma; Manik Varma; |
| 97 | Understanding The Effect of Loss Functions on The Generalization of Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through theoretical analysis, we demonstrate that Softmax and Cosine Contrastive Loss exhibit ? |
Yuanhao Pu; Defu Lian; Xiaolong Chen; Jin Chen; Ze Liu; Enhong Chen; |
| 98 | Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches often rely on additional sequential models to capture dynamics, leading to high computational and memory costs, particularly for large-scale graphs. To address this limitation, we propose the Input Snapshots Fusion based Dynamic Graph Neural Network (SFDyG), which combines Hawkes processes with graph neural networks to capture temporal and structural patterns in dynamic graphs effectively. |
QingGuo Qi; Hongyang Chen; Minhao Cheng; Han Liu; |
| 99 | Tackling The Length Barrier: Dynamic Context Browsing for Knowledge-Intensive Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we propose that the short LLMs have great potentiality for solving knowledge-intensive tasks that have long context, i.e. they can be solved by purely working with oracle short-contexts within the input long-context. |
Hongjin Qian; Zheng Liu; Peitian Zhang; Kelong Mao; Yujia Zhou; Xu Chen; Zhicheng Dou; |
| 100 | Adapting to Generalized Online Label Shift By Invariant Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To optimize this objective, we propose an algorithm employing online ensemble paradigm to perform multi-resolution updates using various historical data windows, thereby enhancing the stability of the representation. |
Yu-Yang Qian; Yi-Han Wang; Zhen-Yu Zhang; Yuan Jiang; Zhi-Hua Zhou; |
| 101 | Quantum Time-index Models with Reservoir for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, inspired by the ability of quantum implicit neural representations to model the high-frequency components of signals with fewer parameters, we propose Quantum Time-Index Models with Reservoir (QuantumTime). |
Wenbo Qiao; Jiaming Zhao; Peng Zhang; |
| 102 | DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly.In this study, we address these challenges by proposing a general framework called DUET, which introduces DU al clustering on the temporal and channel dimensions to Enhance multivariate Time series forecasting. |
Xiangfei Qiu; Xingjian Wu; Yan Lin; Chenjuan Guo; Jilin Hu; Bin Yang; |
| 103 | Fast Causal Discovery By Approximate Kernel-based Generalized Score Functions with Linear Computational Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approximate kernel-based generalized score function with O (n) time and space complexities by using low-rank technique and designing a set of rules to handle the complex composite matrix operations required to calculate the score, as well as developing sampling algorithms for different data types to benefit the handling of diverse data types efficiently. |
Yixin Ren; Haocheng Zhang; Yewei Xia; Hao Zhang; Jihong Guan; Shuigeng Zhou; |
| 104 | ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose ST-MTM, a masked time-series modeling framework with seasonal-trend decomposition, which includes a novel masking method for the seasonal-trend components that incorporates different temporal variations from each component. |
Hyunwoo Seo; Chiehyeon Lim; |
| 105 | Abductive Learning for Neuro-Symbolic Grounded Imitation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we draw inspiration from abductive learning and introduce a novel framework ABductive Imitation Learning (ABIL) that integrates the benefits of data-driven learning and symbolic-based reasoning, enabling long-horizon planning. |
Jie-Jing Shao; Hao-Ran Hao; Xiao-Wen Yang; Yu-Feng Li; |
| 106 | Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. |
Pengyang Shao; Yonghui Yang; Chen Gao; Lei Chen; Kun Zhang; Chenyi Zhuang; Le Wu; Yong Li; Meng Wang; |
| 107 | HeavyLocker: Lock Heavy Hitters in Distributed Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A common challenge encountered by these algorithms is balancing performance with accuracy. To address this challenge, we introduce HeavyLocker, a novel sketch algorithm that takes advantage of a distinct feature of real data streams: the separability of heavy hitters. |
Qilong Shi; Xirui Li; Hanyue Zheng; Tong Yang; Yangyang Wang; Mingwei Xu; |
| 108 | Off-Policy Evaluation and Learning for The Future Under Non-Stationarity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods assume stationarity or depend on restrictive reward-modeling assumptions, leading to significant bias. To address these limitations, we propose a novel estimator named Off-Policy Estimator for the Future Value (OPFV), designed for accurately estimating policy values at any future time point. |
Tatsuhiro Shimizu; Kazuki Kawamura; Takanori Muroi; Yusuke Narita; Kei Tateno; Takuma Udagawa; Yuta Saito; |
| 109 | Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present JEDIS, a framework for detecting illicit drug jargon terms by analyzing their contexts. |
Minkyoo Song; Eugene Jang; Jaehan Kim; Seungwon Shin; |
| 110 | Counterfactual Explanations with Probabilistic Guarantees on Their Robustness to Model Change Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel approach for generating CFEs that provides probabilistic guarantees for any model and change type, while offering interpretable and easy-to-select hyperparameters. |
Ignacy Stundefinedpka; Jerzy Stefanowski; Mateusz Lango; |
| 111 | CLEAR: Addressing Representation Contamination in Multimodal Healthcare Analytics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue of representation contamination, we propose CLEAR, a counterfactual disparity learning model for explicit multimodal EHR analytics. |
Ge Su; Kaiping Zheng; Tiancheng Zhao; Jianwei Yin; |
| 112 | A Unified Invariant Learning Framework for Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that focusing only on the semantic space may not accurately identify these stable features. To address this, we introduce the Unified Invariant Learning (UIL) framework for graph classification. |
Yongduo Sui; Jie Sun; Shuyao Wang; Zemin Liu; Qing Cui; Longfei Li; Xiang Wang; |
| 113 | Handling Feature Heterogeneity with Learnable Graph Patches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a significant challenge is that existing models are unable to address feature heterogeneity in graph data without textual information, which hinders the transferability of graph models across different datasets. To bridge this gap, we propose the concept of learnable graph patches, which we regard as the smallest semantic units of any graph data. |
Yifei Sun; Yang Yang; Xiao Feng; Zijun Wang; Haoyang Zhong; Chunping Wang; Lei Chen; |
| 114 | Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, capturing the interaction relationship between the user features and context features can better predict the user response. To solve the above limitations, we propose a novel model-agnostic Robust Uplift Modeling with Large-Scale Contexts (UMLC) framework for Real-time Marketing. |
Zexu Sun; Qiyu Han; Minqin Zhu; Hao Gong; Dugang Liu; Chen Ma; |
| 115 | R2MR: Review and Rewrite Modality for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These low-quality modalities often lack crucial details or introduce noise to the depiction of item, leading to insufficient or polluted item representation. Therefore, we propose a novel framework R2MR: Review and Rewrite Modality for Recommendation to tackle this issue. |
Gu Tang; Jinghe Wang; Xiaoying Gan; Bin Lu; Ze Zhao; Luoyi Fu; Xinbing Wang; Chenghu Zhou; |
| 116 | Spatially Compact Dense Block Mining in Spatial Tensors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. |
Weike Tang; Dingming Wu; Tsz Nam Chan; Kezhong Lu; |
| 117 | MLDGG: Meta-Learning for Domain Generalization on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast to conventional methodologies, which concentrate on developing specific generalized models, our framework, MLDGG, endeavors to achieve adaptable generalization across diverse domains by integrating cross-multi-domain meta-learning with structure learning and semantic identification. |
Qin Tian; Chen Zhao; Minglai Shao; Wenjun Wang; Yujie Lin; Dong Li; |
| 118 | From Missteps to Mastery: Enhancing Low-Resource Dense Retrieval Through Adaptive Query Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we introduce iGFT, a framework aimed at enhancing low-resource dense retrieval by integrating a three-phase process — Generation, Filtering, and Tuning — coupled with an iterative optimization strategy. |
Zhenyu Tong; Chuan Qin; Chuyu Fang; Kaichun Yao; Xi Chen; Jingshuai Zhang; Chen Zhu; Hengshu Zhu; |
| 119 | GROOT: Effective Design of Biological Sequences with Limited Experimental Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods struggle when labeled data is limited, as training f? with few labeled data points can lead to subpar outputs, offering no advantage over the training data itself. We address this challenge by introducing GROOT, a GRaph-based Latent SmOOThing for Biological Sequence Optimization. |
Thanh V. T. Tran; Nhat Khang Ngo; Viet Anh Nguyen; Truong Son Hy; |
| 120 | How Well Calibrated Are Extreme Multi-label Classifiers? An Empirical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To judge whether an extreme classifier is indeed suited to this task, one can look, for example, to whether it returns calibrated probabilities, which has hitherto not been done in this field. Therefore, this paper aims to establish the current status quo of calibration in XMLC by providing a systematic evaluation, comprising nine models from four different model families across seven benchmark datasets. |
Nasib Ullah; Erik Schultheis; Jinbin Zhang; Rohit Babbar; |
| 121 | Scaling The Vocabulary of Non-autoregressive Models for Fast Generative Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose PIXNAR, a novel approach that expands the target vocabulary of NAR models to include multi-word entities and common phrases (up to 5 million tokens), thereby reducing token dependencies. |
Ravisri Valluri; Akash Kumar Mohankumar; Kushal Dave; Amit Singh; Jian Jiao; Manik Varma; Gaurav Sinha; |
| 122 | Interpretable Prediction and Feature Selection for Survival Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present DyS (pronounced dice”), a new survival analysis model that achieves both strong discrimination and interpretability. |
Mike Van Ness; Madeleine Udell; |
| 123 | Dynamic Causal Structure Discovery and Causal Effect Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop a new framework to model the dynamic causal graph where the causal relations are allowed to be time-varying. |
Jianian Wang; Rui Song; |
| 124 | Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, there exists an asymmetrical reciprocity between underserved and developed regions. To overcome these challenges, we propose a novel cross-silo federated learning framework, named FedHelp, aimed at alleviating geographic health disparities and fortifying the diagnostic capabilities of underserved regions. |
Jiaqi Wang; Ziyi Yin; Quanzeng You; Lingjuan Lyu; Fenglong Ma; |
| 125 | CoopRide: Cooperate All Grids in City-Scale Ride-Hailing Dispatching with Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There exist three key challenges in scaling the cooperation to the whole city: (1) cooperative strategies cause complex interactions among grids, making the grids’ states coupled and complicating the information extraction from the states for decision-making; (2) cooperation among grids requires both within- and cross-grid dispatching, where the priorities of these two types of actions are difficult to balance; (3) the value of cooperation is not only heterogeneous over different pairs of grids, but also varies temporally, adding difficulty to dynamically determine the intensities of cooperation for each pair of grids and obtain the global cooperation rewards. In this paper, we propose the CoopRide framework to solve the above challenges. |
Jingwei Wang; Qianyue Hao; Wenzhen Huang; Xiaochen Fan; Qin Zhang; Zhentao Tang; Bin Wang; Jianye Hao; Yong Li; |
| 126 | An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner. |
Mingzhao Wang; You Zhou; Zhiguang Cao; Yubin Xiao; Xuan Wu; Wei Pang; Yuan Jiang; Hui Yang; Peng Zhao; Yuanshu Li; |
| 127 | Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we consider explicitly generative modeling task distributions placed over task identifiers and propose robustifying fast adaptation from adversarial training. |
Qi (Cheems) Wang; Yiqin Lv; Yixiu Mao; Yun Qu; Yi Xu; Xiangyang Ji; |
| 128 | GraphTool-Instruction: Revolutionizing Graph Reasoning in LLMs Through Decomposed Subtask Instruction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, current Tool-Instruction approaches focus on the tool information and ignore the graph structure information, which leads to significantly inferior performance on small-scale LLMs (less than 8B). To tackle this issue, we propose GraphTool-Instruction, an innovative Instruction-tuning approach that decomposes the graph reasoning task into three distinct subtasks (i.e., graph extraction, tool name identification and tool parameter extraction), and design specialized instructions for each subtask. |
Rongzheng Wang; Shuang Liang; Qizhi Chen; Jiasheng Zhang; Ke Qin; |
| 129 | Embedding Prior Task-specific Knowledge Into Language Models for Context-aware Document Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, since search log data is noisy and contains various user intents and search patterns, such a black-box way may prevent models from fully mastering effective context-aware search knowledge. To solve this problem, we propose LOCK, a PLM-based context-aware document ranking model that explicitly embeds task-specific prior knowledge into PLMs to guide the model optimization. |
Shuting Wang; Yutao Zhu; Zhicheng Dou; |
| 130 | Mitigating Redundancy in Deep Recommender Systems: A Field Importance Distribution Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify the core issue as the lack of a practical score to measure the contribution of feature fields, and propose a distribution-based field optimization framework that adopts importance distribution to provide a comprehensive view for both methods. |
Xianquan Wang; Likang Wu; Zhi Li; Haitao Yuan; Shuanghong Shen; Huibo Xu; Yu Su; Chenyi Lei; |
| 131 | Graph Triple Attention Networks: A Decoupled Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods face two primary challenges: (1) multi-view chaos, which results from coupling multi-view information (positional, structural, attribute), thereby impeding flexible usage and the interpretability of the propagation process. (2) local-global chaos, which arises from coupling local message passing with global attention, leading to issues of overfitting and over-globalizing. To address these challenges, we propose a high-level decoupled perspective of GTs, breaking them down into three components and two interaction levels: positional attention, structural attention, and attribute attention, alongside local and global interaction. |
Xiaotang Wang; Yun Zhu; Haizhou Shi; Yongchao Liu; Chuntao Hong; |
| 132 | Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose BS-VFL, an asynchronous VFL with bounded staleness, to pipeline local computation and statistics transmission, substantially reducing the communication overhead while ensuring favorable model performance. |
Xiong Wang; Yi Zhang; Yuxin Chen; Yuqing Li; Chuanhu Ma; Bo Li; Hai Jin; |
| 133 | Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labels Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, the huge label information gap between training data and targets makes the task challenging. In this study, we formulate the above imperfect information as noisy labels and propose NRdetector, a noise-resilient framework that incorporates confidence-based sample selection, robust segment-level learning, and data-centric point-level detection for multivariate time series anomaly detection. |
Yaxuan Wang; Hao Cheng; Jing Xiong; Qingsong Wen; Han Jia; Ruixuan Song; Liyuan Zhang; Zhaowei Zhu; Yang Liu; |
| 134 | Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We analyze the phenomenon with the insights from CL theory and discover lack of intra-class connectivity in the DG setting causes the deficiency. We thus propose a new paradigm, domain-connecting contrastive learning (DCCL), to enhance the conceptual connectivity across domains and obtain generalizable representations for DG. |
Tianxin Wei; Yifan Chen; Xinrui He; Wenxuan Bao; Jingrui He; |
| 135 | Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With our analysis, we propose the Model Agnostic Causal Active Learning (MACAL) algorithm for batch-wise label acquisition, which aims to reduce both the CEE model’s uncertainty and the post-acquisition distributional imbalance simultaneously at each acquisition step. |
Hechuan Wen; Tong Chen; Guanhua Ye; Li Kheng Chai; Shazia Sadiq; Hongzhi Yin; |
| 136 | FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose FLMarket that integrates a two-stage pricing mechanism with a security protocol to address the utility-privacy conflict. |
Zhenyu Wen; Wanglei Feng; Di Wu; Haozhen Hu; Chang Xu; Bin Qian; Zhen Hong; Cong Wang; Shouling Ji; |
| 137 | Feature Selection for Network Intrusion Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Firstly, trained and deployed models have to process large amounts of unnecessary data, therefore draining potentially costly resources. Secondly, the noise caused by the presence of irrelevant features can, in some cases, impede a model’s ability to detect an attack. In order to deal with these challenges, we present Feature Selection for Network Intrusion Detection (FSNID) a novel information-theoretic method that facilitates the exclusion of non-informative features when detecting network intrusions. |
Charles Westphal; Stephen Hailes; Mirco Musolesi; |
| 138 | Classifying Treatment Responders: Bounds and Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many treatment effect estimation methods have been proposed to identify treatment responders, there are fundamental differences between treatment effect estimation and treatment responder classification, including: (1) accurate causal effect estimation is not necessary for optimal intervention decisions; (2) methods for accurate causal effect estimation do not directly optimize classification loss; (3) treatment responder classification requires identifying joint potential outcomes, while treatment effect estimation focuses on marginal distributions. To fill this gap, we tackle the treatment responder classification problem without assuming monotonicity. |
Anpeng Wu; Haoxuan Li; Chunyuan Zheng; Kun Kuang; Kun Zhang; |
| 139 | Breaking The Memory Wall for Heterogeneous Federated Learning Via Progressive Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper presents ProFL, a new framework that effectively addresses the memory constraints in FL. |
Yebo Wu; Li Li; Cheng-zhong Xu; |
| 140 | ProgDiffusion: Progressively Self-encoding Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a Progressive self-encoded Diffusion model (ProgDiffusion), which simultaneously learns semantic representations and reconstructs observations, does efficient unconditional generation, and produces progressively structured semantic representations. |
Zhangkai Wu; Xuhui Fan; Longbing Cao; |
| 141 | ProST: Prompt Future Snapshot on Dynamic Graphs for Spatio-Temporal Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods fail to fully capture edge information resulting in incomplete and less accurate representations of future snapshot structures. To bridge this gap, we propose ProST, a framework that Prompts future snapshots on dynamic graphs for Spatio-Temporal prediction, which leverages dynamic graph pre-training to generate a premise graph containing historical graph information and then employs prompts on the premise graph to infer explicit future snapshots. |
Kaiwen Xia; Li Lin; Shuai Wang; Qi Zhang; Shuai Wang; Tian He; |
| 142 | Brain Effective Connectivity Estimation Via Fourier Spatiotemporal Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel brain effective connectivity estimation method based on Fourier spatiotemporal attention (FSTA-EC), which combines Fourier attention and spatiotemporal attention to simultaneously capture inter-series (spatial) dynamics and intra-series (temporal) dependencies from high-noise fMRI data. |
Wen Xiong; Jinduo Liu; Junzhong Ji; Fenglong Ma; |
| 143 | ScalaGBM: Memory Efficient GBDT Training for High-Dimensional Data on GPU Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a GPU-based GBDT framework named ScalaGBM, aiming to accelerate high-dimensional data training with less memory usage. |
Borui Xu; Zeyi Wen; Yao Chen; Weiguo Liu; Weng-Fai Wong; Bingsheng He; |
| 144 | Incremental Label Distribution Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conducting LDL for such simultaneous augmentation of feature and label is crucial but rarely studied, particularly when the labeled samples with full observations are limited. In this paper, we propose a novel Incremental Label Distribution Learning (ILDL) method to tackle this brand new LDL problem by continuously transiting discriminative information from the previous model to the current one. |
Chao Xu; Xijia Tang; Hong Tao; Chenping Hou; |
| 145 | Neural Network Pruning for Invariance Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We take a deeper look at neural network pruning from the lens of invariance preservation. |
Derek Xu; Yuanzhou Chen; Yizhou Sun; Wei Wang; |
| 146 | MM-Path: Multi-modal, Multi-granularity Path Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-modal, Multi-granularity Path Representation Learning Framework (MM-Path), which can learn a generic path representation by integrating modalities from both road paths and image paths. |
Ronghui Xu; Hanyin Cheng; Chenjuan Guo; Hongfan Gao; Jilin Hu; Sean Bin Yang; Bin Yang; |
| 147 | Succinct Interaction-Aware Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Shap explanations are easy to interpret, but as they do not incorporate feature interactions, they are also incomplete and potentially misleading. Interaction-aware methods such as nShap report the additive importance of all subsets up to n features. These explanations are complete, but in practice excessively large and difficult to interpret. In this paper, we combine the best of both worlds. |
Sascha Xu; Joscha C\{u}ppers; Jilles Vreeken; |
| 148 | Fast and Accurate Temporal Hypergraph Representation for Hyperedge Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FastHeP, a fast and accurate approach for temporal hyperedge prediction, which can handle large temporal hypergraphs. |
Yuanyuan Xu; Wenjie Zhang; Ying Zhang; Xiwei Xu; Xuemin Lin; |
| 149 | Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the impact of special irrationality factors — such as market sentiment, speculative behavior, market manipulation, and psychological biases — has not been fully considered in existing deep stock forecasting models due to their relative abstraction as well as lack of explicit labels and data description. To fill this gap, we propose UMI, a Universal multi-level Market Irrationality factor model to enhance stock return forecasting. |
Chen Yang; Jingyuan Wang; Xiaohan Jiang; Junjie Wu; |
| 150 | Causal Discovery from Shifted Multiple Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, we propose a causal discovery approach (CausalSME) which automatically identifies pseudo environments and unobserved distribution shifts. |
Dezhi Yang; Guoxian Yu; Jun Wang; Jinglin Zhang; Carlotta Domeniconi; |
| 151 | Mixed Blessing: Class-Wise Embedding Guided Instance-Dependent Partial Label Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To leverage the nuances of IDPLL effectively, for the first time we create class-wise embeddings for each sample, which allow us to explore the relationship of instance-dependent noisy labels, i.e., the class-wise embeddings in the candidate label set should have high similarity, while the class-wise embeddings between the candidate label set and the non-candidate label set should have high dissimilarity. |
Fuchao Yang; Jianhong Cheng; Hui Liu; Yongqiang Dong; Yuheng Jia; Junhui Hou; |
| 152 | CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions Toward Public Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a causality based prediction model, CausalMob, to analyze the causal effects of public events. |
Xiaojie Yang; Hangli Ge; Jiawei Wang; Zipei Fan; Renhe Jiang; Ryosuke Shibasaki; Noboru Koshizuka; |
| 153 | GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Taking inspiration from the success of Low-Rank Adaptation (LoRA) in adapting large language models to various domains, we propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains. |
Zhe-Rui Yang; Jindong Han; Chang-Dong Wang; Hao Liu; |
| 154 | PraFFL: A Preference-Aware Scheme in Fair Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a Preference-aware scheme in Fair Federated Learning (called PraFFL) to generate preference-specific models in real time. |
Rongguang Ye; Wei-Bin Kou; Ming Tang; |
| 155 | Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our analysis identifies two key factors contributing to their success: LLMs’ inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content. Based on these findings, we propose two novel defense mechanisms — boundary awareness and explicit reminder — to address these vulnerabilities in both black-box and white-box settings. |
Jingwei Yi; Yueqi Xie; Bin Zhu; Emre Kiciman; Guangzhong Sun; Xing Xie; Fangzhao Wu; |
| 156 | Inductive Link Prediction on N-ary Relational Facts Via Semantic Hypergraph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As existing methods are mainly entity embedding-based, they struggle to capture entity-independent logical rules. To fill in this gap, we propose an n-ary subgraph reasoning framework for fully inductive link prediction (ILP) on n-ary relational facts. |
Gongzhu Yin; Hongli Zhang; Yuchen Yang; Yi Luo; |
| 157 | Generalizable Recommender System During Temporal Popularity Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we highlight that these methods often overlook a crucial aspect of popularity shifts-their temporal nature-in both training and inference phases. To address this, we propose Temporal Popularity distribution shift generalizABle recommender system (TPAB), a novel disentanglement framework incorporating temporal popularity. |
Hyunsik Yoo; Ruizhong Qiu; Charlie Xu; Fei Wang; Hanghang Tong; |
| 158 | Non-Homophilic Graph Pre-Training and Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, many real-world graphs are non-homophilic-neither strictly nor uniformly homophilic-as they exhibit varying homophilic and heterophilic patterns across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. |
Xingtong Yu; Jie Zhang; Yuan Fang; Renhe Jiang; |
| 159 | Annotation-guided Protein Design with Multi-Level Domain Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Protein-Annotation Alignment Generation PAAG, a multi-modality protein design framework that integrates the textual annotations extracted from protein database for controllable generation in sequence space. |
Chaohao Yuan; Songyou Li; Geyan Ye; Yikun Zhang; Long-Kai Huang; Wenbing Huang; Wei Liu; Jianhua Yao; Yu Rong; |
| 160 | Boosting Explainability Through Selective Rationalization in Pre-trained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such failures severely damage trust in rationalization methods and constrain the application of rationalization techniques on PLMs. In this paper, we find that the homogeneity of tokens in the sentences produced by PLMs is the primary contributor to these problems. |
Libing Yuan; Shuaibo Hu; Kui Yu; Le Wu; |
| 161 | A Structure-aware Invariant Learning Framework for Node-level Graph OOD Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, most of them simply conduct the classic invariant learning objective but lack the consideration of the graph-specific structure information. Therefore, to mitigate their weakness, we propose a Structure-aware Invariant learning framework for Node-level Graph OOD generalization (SING). |
Ruiwen Yuan; Yongqiang Tang; Wensheng Zhang; |
| 162 | Combinatorial Optimization Perspective Based Framework for Multi-behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, when using multi-task learning for prediction, the relationship between the target task and auxiliary tasks is not sufficiently coordinated, resulting in negative information transfer. To address these problems, we propose a novel multi-behavior recommendation framework based on the combinatorial optimization perspective, named COPF. |
Chenhao Zhai; Chang Meng; Yu Yang; Kexin Zhang; Xuhao Zhao; Xiu Li; |
| 163 | Semi-supervised Multi-view Clustering with Active Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper considers the weak pairwise constraints among samples to enhance the clustering performance, and proposes a Semi-supervised Multi-view Clustering method with Active Constraints, SMCAC for short. |
Chao Zhang; Deng Xu; Chunlin Chen; Huaxiong Li; |
| 164 | DimCL: Dimension-Aware Augmentation in Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is difficult to (i) distinguish different dimensions’ efficacy for CL and (ii) bridge the semantic gap between CL and RSs. Overlooking these limitations may cause redundant, false-positive, and irrelevant noise in hidden dimensions of the augmented views.In this paper, we solve the above challenges from the perspective of robust learning and curriculum learning, and propose a novel Dimension-aware augmentation in Ceontrastive Leearning for recommendation (DimCL). |
Chi Zhang; Qilong Han; Qiaoyu Tan; Shengjie Wang; Xiangyu Zhao; Rui Chen; |
| 165 | Generalizing Personalized Federated Graph Augmentation Via Min-max Adversarial Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. |
Liang Zhang; Tao Long; Yang Liu; Lei Zhang; Laizhong Cui; Qingjiang Shi; |
| 166 | PrivDPR: Synthetic Graph Publishing with Deep PageRank Under Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, inspired by the simplicity, effectiveness, and ease of analysis of PageRank, we design PrivDPR, a novel privacy-preserving deep PageRank for graph synthesis. |
Sen Zhang; Haibo Hu; Qingqing Ye; Jianliang Xu; |
| 167 | GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations. To tackle these challenges, this paper introduces GLINT-RU, a lightweight and efficient SRS leveraging a single-layer dense selective Gated Recurrent Units (GRU) module to accelerate inference. |
Sheng Zhang; Maolin Wang; Wanyu Wang; Jingtong Gao; Xiangyu Zhao; Yu Yang; Xuetao Wei; Zitao Liu; Tong Xu; |
| 168 | LLM-Eraser: Optimizing Large Language Model Unlearning Through Selective Pruning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous approaches use gradient ascent (GA) over undesired knowledge to inversely optimize LLMs, which compromises the model’s performance on desired knowledge. To address this limitation, we introduce a novel two-stage approach, named LLM-Eraser, for selectively identifying and editing parameters specifically associated with undesirable knowledge. |
Shengming Zhang; Le Zhang; Jingbo Zhou; Zhi Zheng; Hui Xiong; |
| 169 | IDentity with Locality: An Ideal Hash for Gene Sequence Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We give a simple but practical construction of IDL function families and show that replacing the RH with IDL functions reduces cache misses by a factor of 5x, thus improving query and indexing times of SOTA methods such as COBS and RAMBO by factors up to 2x without compromising their quality. |
Tianyi Zhang; Gaurav Gupta; Aditya Desai; Anshumali Shrivastava; |
| 170 | Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose a novel contrastive learning framework to learn high-quality graph embeddings in hyperbolic space. |
Yifei Zhang; Hao Zhu; Menglin Yang; Jiahong Liu; Rex Ying; Irwin King; Piotr Koniusz; |
| 171 | Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. |
Yutong Zhang; Lixing Chen; Shenghong Li; Nan Cao; Yang Shi; Jiaxin Ding; Zhe Qu; Pan Zhou; Yang Bai; |
| 172 | Can Large Language Models Improve The Adversarial Robustness of Graph Neural Networks? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an LLM-based robust graph structure inference framework, LLM4RGNN. |
Zhongjian Zhang; Xiao Wang; Huichi Zhou; Yue Yu; Mengmei Zhang; Cheng Yang; Chuan Shi; |
| 173 | Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Proceed, a novel proactive model adaptation framework for online time series forecasting. |
Lifan Zhao; Yanyan Shen; |
| 174 | Stable Representation Learning on Graphs from Multiple Environments with Structure Distribution Shift Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, graph structure is very fundamental for GNNs since it greatly affects the message propagation mechanism. In order to solve the above problem, we propose an unsupervised Stable Graph Representation learning (SGR) framework to obtain stable graphs from multiple environments with graph structure bias, and to improve the stability ability of GNN model across environments. |
Tong Zhao; Daixin Wang; Zhiqiang Zhang; Yulin Kang; Jun Zhou; |
| 175 | Understanding Oversmoothing in Diffusion-Based GNNs From The Perspective of Operator Semigroup Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). |
Weichen Zhao; Chenguang Wang; Xinyan Wang; Congying Han; Tiande Guo; Tianshu Yu; |
| 176 | Graph Learning with Distributional Edge Layouts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Distributional Edge Layouts (DELs), a first-of-its-kind method to sample a collection of topological layouts from a Boltzmann distribution under physical energies. |
Xinjian Zhao; Chaolong Ying; Yaoyao Xu; Tianshu Yu; |
| 177 | Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing heterogeneous graph neural networks (HGNNs) fail to simultaneously handle the problems of missing attributes, inaccurate attributes and scarce node labels, which limits their expressiveness. In this paper, we propose a generative self-supervised model GraMI to address these issues simultaneously. |
Yige Zhao; Jianxiang Yu; Yao Cheng; Chengcheng Yu; Yiding Liu; Xiang Li; Shuaiqiang Wang; |
| 178 | Graph Contrastive Learning with Progressive Augmentations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study introduces a novel manner: despite using static graphs, we aim to learn invariant representations by generating a series of evolving contrastive views with temporal coherence and multi-viewpoint insights at various granularities. In this context, we propose the Progressive Augmentation framework for Graph Contrastive Learning (PaGCL). |
Yuhai Zhao; Yejiang Wang; Zhengkui Wang; Wen Shan; Miaomiao Huang; Xingwei Wang; |
| 179 | Towards Context-Aware Traffic Classification Via Time-Wavelet Fusion Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TrafficScope, a time-wavelet fusion network based on Transformer to enhance the performance of encrypted traffic classification. |
Ziming Zhao; Zhuoxue Song; Xiaofei Xie; Zhaoxuan Li; Jiongchi Yu; Fan Zhang; Tingting Li; |
| 180 | Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, to alleviate the data sparsity and enhance representation learning, many efforts have been conducted to integrate contrastive learning (CL) with GNNs. Despite the promising improvements, the contrastive view generation based on structure and representation perturbations in existing methods potentially disrupts the collaborative information in contrastive views, resulting in limited effectiveness of positive alignment.To overcome this issue, we propose CoGCL, a novel framework that aims to enhance graph contrastive learning by constructing contrastive views with stronger collaborative information via discrete codes. |
Bowen Zheng; Junjie Zhang; Hongyu Lu; Yu Chen; Ming Chen; Wayne Xin Zhao; Ji-Rong Wen; |
| 181 | A Two-Stage Pretraining-Finetuning Framework for Treatment Effect Estimation with Unmeasured Confounding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage pretraining-finetuning (TSPF) framework using both large-scale observational data and small-scale RCT data to estimate the CATE in the presence of unmeasured confounding. |
Chuan Zhou; Yaxuan Li; Chunyuan Zheng; Haiteng Zhang; Min Zhang; Haoxuan Li; Mingming Gong; |
| 182 | HRSTORY: Historical News Review Based Online Story Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. |
Renjie Zhou; Haoran Ye; Jian Wan; Yong Liao; |
| 183 | Grid and Road Expressions Are Complementary for Trajectory Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We observe that the two types of trajectories are complementary, providing either region and location information or providing road structure and movement regularity. Therefore, we propose a novel multimodal TRL method, dubbed GREEN, to jointly utilize Grid and Road trajectory Expressions for Effective representatioN learning. |
Silin Zhou; Shuo Shang; Lisi Chen; Peng Han; Christian S. Jensen; |
| 184 | BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose BTFL, a Bayesian-based test-time generalization method for TGFL, which balances generalization and personalization at the sample level during testing. |
Yu Zhou; Bingyan Liu; |
| 185 | RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by findings from prompt tuning research in the NLP domain, which suggest that highly capable pre-trained models need less conditioning signal to achieve desired behaviors, we advocate for strategically incorporating necessary and lightweight feature prompts to certain graph nodes to enhance downstream task performance. |
Jiapeng Zhu; Zichen Ding; Jianxiang Yu; Jiaqi Tan; Xiang Li; Weining Qian; |
| 186 | Contextual Generative Auction with Permutation-level Externalities for Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. |
Ruitao Zhu; Yangsu Liu; Dagui Chen; Zhenjia Ma; Chufeng Shi; Zhenzhe Zheng; Jie Zhang; Jian Xu; Bo Zheng; Fan Wu; |
| 187 | Exploring Feature-based Knowledge Distillation for Recommender System: A Frequency Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze the feature-based knowledge distillation for recommendation from the frequency perspective. |
Zhangchi Zhu; Wei Zhang; |
| 188 | HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An effective auto-tuning solution is required to identify a viable model within 2-3 days, rather than the extended timelines typically associated with existing approaches. In this paper, we introduce a practical auto-tuning system named HyperZero that addresses these time constraints while effectively solving the unique challenges inherent in modern recommendation systems. |
Xufeng Cai; Ziwei Guan; Lei Yuan; Ali Selman Aydin; Tengyu Xu; Boying Liu; Wenbo Ren; Renkai Xiang; Songyi He; Haichuan Yang; Serena Li; Mingze Gao; Yue Weng; Ji Liu; |
| 189 | Powerformer: A Section-adaptive Transformer for Power Flow Adjustment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. |
Kaixuan Chen; Wei Luo; Shunyu Liu; Yaoquan Wei; Yihe Zhou; Yunpeng Qing; Quan Zhang; Yong Wang; Jie Song; Mingli Song; |
| 190 | An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, such a problem is challenging due to the complex bidding environment faced by diverse advertisers. To address this challenge, we introduce ABPlanner, a few-shot adaptable budget planner designed to improve budget-constrained auto-bidding. |
Zhijian Duan; Yusen Huo; Tianyu Wang; Zhilin Zhang; Yeshu Li; Chuan Yu; Jian Xu; Bo Zheng; Xiaotie Deng; |
| 191 | ForTune: Running Offline Scenarios to Estimate Impact on Business Metrics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these experiments can be time-consuming and costly, particularly when assessing impacts on key business metrics like retention or long-term value.Offline experimentation allows for rapid iteration and testing but often lacks the same level of confidence and clarity regarding business metrics impact. To address this, we introduce a novel, lightweight, and flexible approach called scenario analysis. |
Georges Dupret; Konstantin Sozinov; Carmen Barcena Gonzalez; Ziggy Zacks; Amber Yuan; Ben Carterette; Manuel Mai; Andrey Gatash; Gwo Liang Lien; Shubham Bansal; Roberto Sanchis-Ojeda; Mounia Lalmas; |
| 192 | Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe a novel approach that combines fast experiments (e.g., biased experiments run only for a few hours or days) and/or offline proxies (e.g., off-policy evaluation) with long-running, slow experiments to perform sequential, Bayesian optimization over large action spaces in a short amount of time. |
Qing Feng; Samuel Daulton; Benjamin Letham; Maximilian Balandat; Eytan Bakshy; |
| 193 | Multi-Task Combinatorial Bandits for Budget Allocation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to formulate budget allocation as a multi-task combinatorial bandit problem and introduce a novel online budget allocation system. |
Lin Ge; Yang Xu; Jianing Chu; David Cramer; Fuhong Li; Kelly Paulson; Rui Song; |
| 194 | TEMPER: Capturing Consistent and Fluctuating TEMPoral User Behaviour for EtheReum Phishing Scam Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. |
Medhasree Ghosh; Chirag Dinesh Jain; Raju Halder; Joydeep Chandra; |
| 195 | Efficient Multi-Expert Tabular Language Model for Banking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an efficient multi-expert TaLM architecture and training method tailored for multi-domain databases and modest infrastructure. |
Yue Guo; Wentao Zhang; Xiaojun Zhang; Vincent W. Zheng; Yi Yang; |
| 196 | Learning Adaptive Reserve Price in Display Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we report a novel adaptive reserve price strategy based on reinforcement learning (RL). |
Kun Hu; Shumin Zhang; Lixia Wu; Yongjun Dai; Minfang Lu; Yuting Qiang; Minglong Li; |
| 197 | Synthetic Survey Data Generation and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Depending on the nature and scale, survey data sharing comes with privacy risks, and data collectors and agencies are constrained by disclosure permissions, limiting usage across research groups and institutes. Previous methods for synthetic data generation and deidentification may not entirely prevent information disclosures, or they may sacrifice data quality and granularity.Using a large-scale national voter file at both national and state levels, this paper introduces an end-to-end pipeline to streamline synthetic data generation and evaluation for survey researchers. |
Yanru Jiang; Siyu Liang; Junwon Choi; |
| 198 | Large Vison-Language Foundation Model in Baidu AIGC Image Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we establish a fundamental 10B multimodal model foundation for multimodal generation tasks and propose a scene-based alignment learning approach called conditional sample supervised fine-tuning for downstream generation tasks. |
Zhipeng Jin; Wen Tao; Yafei Li; Yi Yang; Cong Han; Shuanglong Li; Lin Liu; |
| 199 | YaART: Yet Another ART Rendering Technology Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). |
Sergey Kastryulin; Artem Konev; Alexander Shishenya; Eugene Lyapustin; Artem Khurshudov; Alexander Tselousov; Nikita Vinokurov; Denis Kuznedelev; Alexander Markovich; Grigoriy Livshits; Alexey Kirillov; Anastasiia Tabisheva; Liubov Chubarova; Marina Kaminskaia; Alexander Ustyuzhanin; Artemii Shvetsov; Daniil Shlenskii; Valerii Startsev; Dmitrii Kornilov; Mikhail Romanov; Dmitry Baranchuk; Artem Babenko; Sergei Ovcharenko; Valentin Khrulkov; |
| 200 | TGDataset: Collecting and Exploring The Largest Telegram Channels Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces the TGDataset, the most extensive publicly available collection of Telegram channels, comprising 120,979 channels and over 400 million messages. |
Massimo La Morgia; Alessandro Mei; Alberto Maria Mongardini; |
| 201 | LLMLight: Large Language Models As Traffic Signal Control Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents LLMLight, a novel framework employing Large Language Models (LLMs) as decision-making agents for TSC. |
Siqi Lai; Zhao Xu; Weijia Zhang; Hao Liu; Hui Xiong; |
| 202 | A Deep Subgrouping Framework for Precision Drug Repurposing Via Emulating Clinical Trials on Real-world Patient Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach may overlook promising drugs that benefit specific subgroups but lack notable treatment effects across the entire population, potentially limiting the number of repurposable candidates identified. To address this, we introduce STEDR, a novel drug repurposing framework that integrates subgroup analysis with treatment effect estimation. |
Seungyeon Lee; Ruoqi Liu; Feixiong Cheng; Ping Zhang; |
| 203 | ECGrecover: A Deep Learning Approach for Electrocardiogram Signal Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. |
Alex Lence; Federica Granese; Ahmad Fall; Blaise Hanczar; Joe-Elie Salem; Jean-Daniel Zucker; Edi Prifti; |
| 204 | MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The intricacy of C2C recommendation systems is further accentuated by the dual roles users assume as both sellers and buyers, introducing a spectrum of less uniform and varied inputs. Addressing this, we introduce MerRec, the first large-scale dataset specifically for C2C recommendations, sourced from the Mercari e-commerce platform, covering millions of users and products over 6 months in 2023. |
Lichi Li; Zainul Abi Din; Zhen Tan; Sam London; Tianlong Chen; Ajay Daptardar; |
| 205 | Contrastive Learning for Inventory Add Prediction at Fliggy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Contrastive Learning framework for Inventory Add Prediction at Fliggy (CL4IAP), which consists of the Joint Pay-Accept Prediction Module, the Data Augmentation Module, and the Contrastive Learning Module. |
Manwei Li; Detao Lv; Yao Yu; Zihao Jiao; |
| 206 | FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing RL algorithms face several real-world challenges that hinder their practical deployment in TSC: (1) Sensor accuracy deteriorates with increased sensor detection range, and data transmission is prone to noise, potentially resulting in unsafe TSC decisions. (2) During the training of online RL, interactions with the environment could be unstable, potentially leading to inappropriate traffic signal phase (TSP) selection and traffic congestion. (3) Most current TSC algorithms focus only on TSP decisions, overlooking the critical aspect of phase duration, affecting safety and efficiency. To overcome these challenges, we propose a robust two-stage fuzzy approach called FuzzyLight, which integrates compressed sensing and RL for TSC deployment. |
Mingyuan Li; Jiahao Wang; Bo Du; Jun Shen; Qiang Wu; |
| 207 | Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we re-examine the SID problem and identify two prevalent biases in current training paradigms, i.e., weakened artifact features and overfitted artifact features. |
Ouxiang Li; Jiayin Cai; Yanbin Hao; Xiaolong Jiang; Yao Hu; Fuli Feng; |
| 208 | RankElectra: Semi-supervised Pre-training of Learning-to-Rank Electra for Web-scale Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Following the success of Electra in representation learning for natural language processing (NLP), this work proposes RankElectra that pre-trains the LTR model as a discriminator module inside a generative learning framework. |
Yuchen Li; Haoyi Xiong; Yongqi Zhang; Jiang Bian; Tianhao Peng; Xuhong Li; Shuaiqiang Wang; Linghe Kong; Dawei Yin; |
| 209 | Automatic Radiotherapy Treatment Planning with Deep Functional Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: First, a separate sub-network must be designed for each organ, rendering it difficult to apply to patients with an inconsistent number of structures. Second, the low signal-to-noise input and discrete action space result in low training efficiency. To address these issues, we propose an organ-sharing network that contains a functional embedding layer to extract curve features of the dose-volume histogram. |
Bin Liu; Yu Liu; Zhiqian Li; Jianghong Xiao; Guosheng Yin; Huazhen Lin; |
| 210 | Scenario Shared Instance Modeling for Click-through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given these challenges, we first show in a motivating experiment that it may be beneficial to explicitly select a reasonable set of shared instances that can affect parameter optimization in all scenarios during the training of MSR, i.e., to explicitly obtain the critical information required for MSR from the data level. Then, this paper proposes SSIM with an adaptive selection network. |
Dugang Liu; Chaohua Yang; Yuwen Fu; Xing Tang; Gongfu Li; Fuyuan Lyu; Xiuqiang He; Zhong Ming; |
| 211 | LinkSAGE: Optimizing Job Matching Using Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present LinkSAGE, an innovative framework that integrates Graph Neural Networks (GNNs) into large-scale personalized job matching systems, designed to address the complex dynamics of LinkedIn’s extensive professional network. |
Ping Liu; Haichao Wei; Xiaochen Hou; Jianqiang Shen; Shihai He; Qianqi Shen; Zhujun Chen; Fedor Borisyuk; Daniel Hewlett; Liang Wu; Srikant Veeraraghavan; Alex Tsun; Chengming Jiang; Wenjing Zhang; |
| 212 | Session-Level Dynamic Ad Load Optimization Using Offline Robust Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop an offline deep Q-network (DQN)-based framework that effectively mitigates confounding bias in dynamic systems and demonstrates more than 80\% offline gains compared to the best causal learning-based production baseline. |
Tao Liu; Qi Xu; Wei Shi; Zhigang Hua; Shuang Yang; |
| 213 | Roadside Multi-LiDAR Data Fusion for Enhanced Traffic Safety Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The problem is more complex when heterogeneous sensors differ in resolution and are positioned arbitrarily on a traffic intersection.We propose a calibration technique to fuse multiple LiDARs. |
Md Parvez Mollah; Biplob Debnath; Murugan Sankaradas; Srimat Chakradhar; Abdullah Mueen; |
| 214 | Using Instruction-Tuned LMs for Scalable Use Case-Based Shopping – Where Customers Meet Their Needs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we utilize instruction-tuned LMs to primarily focus on the first two steps. |
Rajdeep Mukherjee; Sonali Singh; Sachin Farfade; |
| 215 | Understanding Team Collapse Via Probabilistic Graphical Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we develop a graphical model to capture team dynamics. |
Iasonas Nikolaou; Konstantinos Pelechrinis; Evimaria Terzi; |
| 216 | Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an advanced, explainable machine learning method that utilizes Light Detection and Ranging (LiDAR) data for effective wake vortex detection. |
Zhan Qu; Shuzhou Yuan; Michael F\{a}rber; Marius Brennfleck; Niklas Wartha; Anton Stephan; |
| 217 | Towards Web-scale Recommendations with LLMs: From Quality-aware Ranking to Candidate Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present our approach for leveraging Large Language Models (LLMs) for enhancing our web-scale recommendation system. |
Jaidev Shah; Iman Barjasteh; Amey Barapatre; Rana Forsati; Gang Luo; Fan Wu; Yuan Fang; Xue Deng; Blake Shepard; Ronak Shah; Linjun Yang; Hongzhi Li; |
| 218 | SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To navigate the flood of data, we propose a framework to select the most semantically diverse and important dataset portion. |
Maying Shen; Nadine Chang; Sifei Liu; Jose M. Alvarez; |
| 219 | AntAkso: Claims Management System for Health Insurance in Alipay Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is a noticeable lack of shared relevant experience from previous research in this field. In response to this challenge, we introduce AntAkso, a robust claims management system specifically designed for health insurance operations within Alipay. |
Qitao Shi; Jun Zhou; Ya-Lin Zhang; Longfei Li; Chaoyi Ma; Yifan Wu; Xiaobo Qin; |
| 220 | CATER: A Cluster-Based Alternative-Term Recommendation Framework for Large-Scale Web Search at NAVER Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce four design considerations (DCs) that were considered when designing and implementing CATER. |
Jiwon Son; Jaeyoon Kim; Taekin Kim; Yeon-Chang Lee; Sang-Wook Kim; |
| 221 | A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A major challenge with neural models for this task is the lack of high-quality training data, as the annotations for attribute-value pairs in the available datasets are often incomplete. To address this, we introduce GenToC, a model designed for training directly with partially-labeled data, eliminating the necessity for a fully annotated dataset. |
D. Subhalingam; Keshav Kolluru; Saurabh Singal; |
| 222 | Unifying Adversarial Multi-Deconfounded Learning Paradigm for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The presence of multiple confounders further escalates the complexity and challenges of debiasing learning. To tackle this issue, we introduce the Adversarial Multi-Deconfounded (AMD) Learning Paradigm, a generic training framework designed to eliminate biases from multiple confounders. |
Zixun Sun; Mingye Xu; Guanming Liang; Qi Liu; |
| 223 | Struct-X: Enhancing The Reasoning Capabilities of Large Language Models in Structured Data Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These elements can overburden and disrupt the generation process of LLMs, complicating the extraction of relevant insights and the production of coherent outputs. To address this, we propose Struct-X, a novel framework that operates through five key phases: ”read-model-fill-reflect-reason” efficiently enabling LLMs to utilize structured data. |
Xiaoyu Tan; Haoyu Wang; Xihe Qiu; Leijun Cheng; Yuan Cheng; Wei Chu; Yinghui Xu; Yuan Qi; |
| 224 | Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These kinds of low-quality videos, which are widely present in industrial environments, have been overlooked in academic research before, and accurately identifying them is very challenging. In this paper, we introduce a Multi-Branch Collaborative learning Network (MBCN) to tackle the above issues. |
Hengzhu Tang; Zefeng Zhang; Zhiping Li; Zhenyu Zhang; Xing Wu; Li Gao; Suqi Cheng; Dawei Yin; |
| 225 | Generative Retrieval for Book Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Splitting book information and treating it as a collection of separate segments for learning might result in a loss of hierarchical information.We propose an effective Generative retrieval framework for Book Search (GBS) that features two main components: data augmentation and outline-oriented book encoding. |
Yubao Tang; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Shihao Liu; Shuaiqiang Wang; Dawei Yin; Xueqi Cheng; |
| 226 | Cross-Species Insights: Transforming Drug Efficacy from Rats to Humans Using Tissue-Specific Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While initial outcomes may appear promising in animal studies, ensuring similar effectiveness in humans, especially across specific target tissues, presents a significant obstacle. To address this pressing concern, we introduce a novel generative model tailored to optimize molecules that have demonstrated efficacy in rats for enhanced performance in specific human tissues. |
Sally Turutov; Kira Radinsky; |
| 227 | Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This can cause these intrinsic tendencies to become a shortcut bias for the model, leading to insufficient mining of the most concerned user-item preferences. To solve this challenge, we introduce the Breaker model. |
Chao Wang; Yue Zheng; Yujing Zhang; Yan Feng; Zhe Wang; Xiaowei Shi; An You; Yu Chen; |
| 228 | HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the practical problems and the lessons learned at short-video services from Kuaishou. |
Xu Wang; Jiangxia Cao; Zhiyi Fu; Kun Gai; Guorui Zhou; |
| 229 | Producer-Side Experiments Based on Counterfactual Interleaving Designs for Online Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the limitations of current methods and propose the principles of consistency and monotonicity for designing producer-side experiments in online recommender systems. |
Yan Wang; Shan Ba; |
| 230 | SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. |
Yuanchun Wang; Jifan Yu; Zijun Yao; Jing Zhang; Yuyang Xie; Shangqing Tu; Yiyang Fu; Youhe Feng; Jinkai Zhang; Jingyao Zhang; Bowen Huang; Yuanyao Li; Huihui Yuan; Lei Hou; Juanzi Li; Jie Tang; |
| 231 | Beyond Item Dissimilarities: Diversifying By Intent in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in this work, we demonstrate the benefits of going beyond item-level similarities by utilizing higher-level user understanding-specifically, user intents that persist across multiple interactions or recommendation sessions-in diversification. |
Yuyan Wang; Cheenar Banerjee; Samer Chucri; Fabio Soldo; Sriraj Badam; Ed H. Chi; Minmin Chen; |
| 232 | DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions. |
Hao Wu; Haomin Wen; Guibin Zhang; Yutong Xia; Yuxuan Liang; Yu Zheng; Qingsong Wen; Kun Wang; |
| 233 | LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This results in labor-intensive processes and hampers timely updates. To address these challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm for city-scale lane-level map updating. |
Deguo Xia; Weiming Zhang; Xiyan Liu; Wei Zhang; Chenting Gong; Xiao Tan; Jizhou Huang; Mengmeng Yang; Diange Yang; |
| 234 | Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the straightforward adaptation of existing prediction models often falls short, primarily due to (I) a lack of consideration for the intuition behind AOI divisions, and (II) a reliance solely on fully observed historical data, which may not inform future trends. To overcome the above pitfalls, leveraging rich AOI data and advanced parcel travel time estimation services in JD Logistics, this paper introduces a novel framework called Dual-view Prediction Networks (DualPNs). |
Yinfeng Xiang; Jiangyi Fang; Chao Li; Haitao Yuan; Yiwei Song; Jiming Chen; |
| 235 | Scalable Area Difficulty Assessment with Knowledge-enhanced AI for Nationwide Logistics Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the significant expenses associated with ground truth data collection limit the capabilities of current machine learning methods. In this paper, we consider a frequently overlooked resource, i.e., the workers’ firsthand knowledge of areas, to address this problem in a human-AI collaboration fashion. |
Zejun Xie; Wenjun Lyu; Yiwei Song; Haotian Wang; Guang Yang; Yunhuai Liu; Tian He; Desheng Zhang; Guang Wang; |
| 236 | Mutual Information-aware Knowledge Distillation for Short Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Vanilla distillation methods mitigate the training-inference inconsistency, struggling to capture the dynamic dependence between context cumulative effects and user feedback. To address this problem, we propose the Mutual Information-aware Knowledge Distillation (MIKD) framework, which fuses such effects and user-item matching degrees by evaluating their impacts on user feedback based on mutual information estimation. |
Han Xu; Taoxing Pan; Zhiqiang Liu; Xiaoxiao Xu; |
| 237 | Disclosing Actual Controller Based on Equity Knowledge Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an AC disclosure method based on Equity Knowledge Graph Learning (EKGL). |
Qingying Xu; Liang Hong; Mingxuan Shen; Baokun Yi; |
| 238 | Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN). |
Xiang Xu; Hao Wang; Wei Guo; Luankang Zhang; Wanshan Yang; Runlong Yu; Yong Liu; Defu Lian; Enhong Chen; |
| 239 | AddrLLM: Address Rewriting Via Large Language Model on Nationwide Logistics Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. |
Qinchen Yang; Zhiqing Hong; Dongjiang Cao; Haotian Wang; Zejun Xie; Tian He; Yunhuai Liu; Yu Yang; Desheng Zhang; |
| 240 | SWaT: Statistical Modeling of Video Watch Time Through User Behavior Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we for the first time take on a user-centric perspective to model video watch time, from which we propose a white-box statistical framework that directly translates various user behavior assumptions in watching (short) videos into statistical watch time models. |
Shentao Yang; Haichuan Yang; Linna Du; Adithya Ganesh; Bo Peng; Boying Liu; Serena Li; Ji Liu; |
| 241 | SepsisCalc: Integrating Clinical Calculators Into Early Sepsis Prediction Via Dynamic Temporal Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, we propose to mimic clinicians’ workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. |
Changchang Yin; Shihan Fu; Bingsheng Yao; Thai-Hoang Pham; Weidan Cao; Dakuo Wang; Jeffrey Caterino; Ping Zhang; |
| 242 | BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, there are no existing backdoor benchmarks targeting multimodal applications or related tasks.In order to facilitate the research in multimodal backdoor, we introduce BackdoorMBTI, the first backdoor learning toolkit and benchmark designed for multimodal evaluation across three representative modalities from eleven commonly used datasets. |
Haiyang Yu; Tian Xie; Jiaping Gui; Pengyang Wang; Pengzhou Cheng; Ping Yi; Yue Wu; |
| 243 | Instruction Semantics Enhanced Dual-Flow Graph Model for GPU Error Resilience Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address those problems, this paper introduces a novel paradigm, namely InstrDGM, for efficiently predicting GPU error resilience. |
Pengfei Yu; Jingjing Gu; Dazhong Shen; Xin Dong; Yang Liu; Hui Xiong; |
| 244 | NoteLLM-2: Multimodal Large Representation Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While leveraging existing Multimodal Large Language Models (MLLMs) for such tasks is promising, challenges arise due to their delayed release compared to corresponding LLMs and the inefficiency in representation tasks. To address these issues, we propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation. |
Chao Zhang; Haoxin Zhang; Shiwei Wu; Di Wu; Tong Xu; Xiangyu Zhao; Yan Gao; Yao Hu; Enhong Chen; |
| 245 | Large-scale Human Mobility Data Regeneration for Open Urban Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of the publicly available datasets are actually only records of discontinuous trajectories of a very small portion of urban citizens in asynchronous time due to the limited usage of apps for location data collection or the limited number of volunteers. To address this problem and empower open urban research, this paper constructs a high-quality human mobility dataset by generating large-scale citizen trajectories based on massive cellular signaling data. |
Ruixing Zhang; Yunqi Liu; Liangzhe Han; Leilei Sun; Chuanren Liu; Jibin Wang; Weifeng Lv; |
| 246 | MentorPDM: Learning Data-Driven Curriculum for Multi-Modal Predictive Maintenance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing PDM systems face two primary challenges: 1) Irregular Signal Acquisition, where data collection from the sensors is intermittent, and 2) Signal Heterogeneity, where the full spectrum of sensor modalities is not effectively integrated. To address these challenges, we propose a Curriculum Learning Framework for Multi-Modal Predictive Maintenance – MentorPDM. |
Shuaicheng Zhang; Tuo Wang; Stephen Adams; Sanmitra Bhattacharya; Sunil Reddy Tiyyagura; Edward Bowen; Balaji Veeramani; Dawei Zhou; |
| 247 | Multi-period Learning for Financial Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Multi-period Learning Framework (MLF) to enhance financial TSF performance. |
Xu Zhang; Zhengang Huang; Yunzhi Wu; Xun Lu; Erpeng Qi; Yunkai Chen; Zhongya Xue; Qitong Wang; Peng Wang; Wei Wang; |
| 248 | MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address these issues, we introduce two large-scale personalized health-aware food recommendation benchmarks at the first attempt. Building on this, we propose a novel framework called the Multi-Objective Personalized Interpretable Health-aware Food Recommendation System (MOPI-HFRS). |
Zheyuan Zhang; Zehong Wang; Tianyi Ma; Varun Sameer Taneja; Sofia Nelson; Nhi Ha Lan Le; Keerthiram Murugesan; Mingxuan Ju; Nitesh V. Chawla; Chuxu Zhang; Yanfang Ye; |
| 249 | Awaking The Slides: A Tuning-free and Knowledge-regulated AI Tutoring System Via Language Model Coordination Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the problem of discovering effective designs that convert a slide into an interactive lecture. |
Daniel Zhang-Li; Zheyuan Zhang; Jifan Yu; Joy Jia Yin Lim; Shangqing Tu; Linlu Gong; Haohua Wang; Zhiyuan Liu; Huiqin Liu; Lei Hou; Juanzi Li; |
| 250 | Prices Do Matter: Modeling Price Competitiveness for Online Hotel Industry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel’s pricing competitiveness within the marketplace. |
Ruitao Zhu; Wendong Xiao; Yao Yu; Yangsu Liu; Zhenzhe Zheng; Shuqi Zhang; Dong Li; Fan Wu; |