Kernel biclustering algorithm in Hilbert spaces.

Dino Sejdinovic University of Adelaide, Australia https://mathscinet.ams.org/mathscinet/MRAuthorID/1083001 https://mathgenealogy.org/id.php?id=249427 https://openreview.net/profile?id=~Dino_Sejdinovic1 https://orcid.org/0000-0001-5547-9213 https://www.wikidata.org/entity/Q59561447

Marcos Matabuena Juan Carlos Vidal Oscar Hernan Madrid Padilla Dino Sejdinovic Kernel biclustering algorithm in Hilbert spaces. 2026 June 479-520 20 Adv. Data Anal. Classif. 2 https://doi.org/10.1007/s11634-024-00619-0 db/journals/adac/adac20.html#MatabuenaVPS26 streams/journals/adac

Yuqi Zhang Krikamol Muandet Dino Sejdinovic Edwin Fong Siu Lun Chau Instrumental and Proximal Causal Inference with Gaussian Processes. 2026 March abs/2603.02159 CoRR https://doi.org/10.48550/arXiv.2603.02159 db/journals/corr/corr2603.html#abs-2603-02159 streams/journals/corr

Erdun Gao Liang Zhang Jake Fawkes Aoqi Zuo Wenqin Liu Haoxuan Li 0001 Mingming Gong Dino Sejdinovic Observationally Informed Adaptive Causal Experimental Design. 2026 March abs/2603.03785 CoRR https://doi.org/10.48550/arXiv.2603.03785 db/journals/corr/corr2603.html#abs-2603-03785 streams/journals/corr

Luca Bergen Dino Sejdinovic Vanessa Didelez The Generalised Kernel Covariance Measure. 2026 April abs/2604.03721 CoRR https://doi.org/10.48550/arXiv.2604.03721 db/journals/corr/corr2604.html#abs-2604-03721 streams/journals/corr

Boming Xia Liming Zhu 0001 Erdun Gao Qinghua Lu 0001 Minhui Xue 0001 Dino Sejdinovic Uncertainty Propagation in LLM-Based Systems. 2026 April abs/2604.23505 CoRR https://doi.org/10.48550/arXiv.2604.23505 db/journals/corr/corr2604.html#abs-2604-23505 streams/journals/corr

Peter Moskvichev Siu Lun Chau Dino Sejdinovic Measuring Differences between Conditional Distributions using Kernel Embeddings. 2026 May abs/2605.02260 CoRR https://doi.org/10.48550/arXiv.2605.02260 db/journals/corr/corr2605.html#abs-2605-02260 streams/journals/corr

Anh Vu Nguyen Dino Sejdinovic Tat-Jun Chin Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing. 2026 May abs/2605.05590 CoRR https://doi.org/10.48550/arXiv.2605.05590 db/journals/corr/corr2605.html#abs-2605-05590 streams/journals/corr

Wenqin Liu Weizhi Quan Aoqi Zuo Erdun Gao Vu Nguyen Dino Sejdinovic Howard D. Bondell Mingming Gong TimeLAVA: Learning-Agnostic Valuation for Time Series Data. 2026 June abs/2606.18729 CoRR https://doi.org/10.48550/arXiv.2606.18729 db/journals/corr/corr2606.html#abs-2606-18729 streams/journals/corr

Boming Xia Liming Zhu 0001 Zhenchang Xing Qinghua Lu 0001 Dino Sejdinovic Xiwei Xu 0001 Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents. 2026 June abs/2606.20631 CoRR https://doi.org/10.48550/arXiv.2606.20631 db/journals/corr/corr2606.html#abs-2606-20631 streams/journals/corr

Bao Gia Doan Afshar Shamsi Xiao-Yu Guo Arash Mohammadi 0001 Hamid Alinejad-Rokny Dino Sejdinovic Damien Teney Damith C. Ranasinghe Ehsan Abbasnejad Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks. 16298-16307 2025 AAAI https://doi.org/10.1609/aaai.v39i15.33790 conf/aaai/2025 db/conf/aaai/aaai2025.html#DoanSG0ASTRA25 Siu Lun Chau Antonin Schrab Arthur Gretton Dino Sejdinovic Krikamol Muandet Credal Two-Sample Tests of Epistemic Uncertainty. 127-135 2025 AISTATS https://proceedings.mlr.press/v258/chau25a.html conf/aistats/2025 db/conf/aistats/aistats2025.html#ChauSGSM25 streams/conf/aistats Peter Moskvichev Dino Sejdinovic All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. 274-287 2025 AI (1) https://doi.org/10.1007/978-981-95-4969-6_21 conf/ausai/2025-1 db/conf/ausai/ai2025-1.html#MoskvichevS25 streams/conf/ausai Russell Tsuchida Jiawei Liu Cheng Soon Ong Dino Sejdinovic Squared families are useful conjugate priors. 2025 NeurIPS http://papers.nips.cc/paper_files/paper/2025/hash/22ba683e3620f0b3d4df9d69db8de6a7-Abstract-Conference.html conf/nips/2025 db/conf/nips/neurips2025.html#TsuchidaLOS25 Daokun Zhang Russell Tsuchida Dino Sejdinovic Label Distribution Learning using the Squared Neural Family on the Probability Simplex. 4872-4888 2025 UAI https://proceedings.mlr.press/v286/zhang25a.html conf/uai/2025 db/conf/uai/uai2025.html#ZhangTS25 streams/conf/uai

Peter Moskvichev Dino Sejdinovic All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. 2025 February abs/2502.11465 CoRR https://doi.org/10.48550/arXiv.2502.11465 db/journals/corr/corr2502.html#abs-2502-11465 streams/journals/corr

Veit Wild James Wu Dino Sejdinovic Jeremias Knoblauch Near-Optimal Approximations for Bayesian Inference in Function Space. 2025 February abs/2502.18279 CoRR https://doi.org/10.48550/arXiv.2502.18279 db/journals/corr/corr2502.html#abs-2502-18279 streams/journals/corr

Russell Tsuchida Jiawei Liu Cheng Soon Ong Dino Sejdinovic Squared families: Searching beyond regular probability models. 2025 March abs/2503.21128 CoRR https://doi.org/10.48550/arXiv.2503.21128 db/journals/corr/corr2503.html#abs-2503-21128 streams/journals/corr

Motonobu Kanagawa Philipp Hennig Dino Sejdinovic Bharath K. Sriperumbudur Gaussian Processes and Reproducing Kernels: Connections and Equivalences. 2025 June abs/2506.17366 CoRR https://doi.org/10.48550/arXiv.2506.17366 db/journals/corr/corr2506.html#abs-2506-17366 streams/journals/corr

Erdun Gao Jake Fawkes Dino Sejdinovic Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation. 2025 September abs/2509.21866 CoRR https://doi.org/10.48550/arXiv.2509.21866 db/journals/corr/corr2509.html#abs-2509-21866 streams/journals/corr

Erdun Gao Dino Sejdinovic ActiveCQ: Active Estimation of Causal Quantities. 2025 September abs/2509.24293 CoRR https://doi.org/10.48550/arXiv.2509.24293 db/journals/corr/corr2509.html#abs-2509-24293 streams/journals/corr

Robert Hu Dino Sejdinovic Robin J. Evans 0002 A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. 160:1-160:56 2024 25 J. Mach. Learn. Res. https://jmlr.org/papers/v25/21-1409.html db/journals/jmlr/jmlr25.html#HuSE24

Jake Fawkes Robert Hu Robin J. Evans 0002 Dino Sejdinovic Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. 2024 2024 Trans. Mach. Learn. Res. https://openreview.net/forum?id=5g5zFVj33K db/journals/tmlr/tmlr2024.html#FawkesHES24

Russell Tsuchida Cheng Soon Ong Dino Sejdinovic Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. 20559-20566 2024 AAAI https://doi.org/10.1609/aaai.v38i18.30041 conf/aaai/2024 db/conf/aaai/aaai2024.html#TsuchidaOS24 Eiki Shimizu Kenji Fukumizu Dino Sejdinovic Neural-Kernel Conditional Mean Embeddings. 2024 ICML https://proceedings.mlr.press/v235/shimizu24a.html https://openreview.net/forum?id=0wso32h0jc conf/icml/2024 db/conf/icml/icml2024.html#ShimizuFS24 45040-45059 Rafael Oliveira 0001 Dino Sejdinovic David Howard Edwin V. Bonilla Bayesian Adaptive Calibration and Optimal Design. 2024 NeurIPS http://papers.nips.cc/paper_files/paper/2024/hash/673b9f8cba4ee9f15b88656a69761631-Abstract-Conference.html conf/nips/2024 db/conf/nips/neurips2024.html#0001SHB24

Russell Tsuchida Cheng Soon Ong Dino Sejdinovic Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. 2024 abs/2402.09608 CoRR https://doi.org/10.48550/arXiv.2402.09608 db/journals/corr/corr2402.html#abs-2402-09608

Eiki Shimizu Kenji Fukumizu Dino Sejdinovic Neural-Kernel Conditional Mean Embeddings. 2024 abs/2403.10859 CoRR https://doi.org/10.48550/arXiv.2403.10859 db/journals/corr/corr2403.html#abs-2403-10859

Rafael Oliveira 0001 Dino Sejdinovic David Howard Edwin V. Bonilla Bayesian Adaptive Calibration and Optimal Design. 2024 abs/2405.14440 CoRR https://doi.org/10.48550/arXiv.2405.14440 db/journals/corr/corr2405.html#abs-2405-14440

Bao Gia Doan Afshar Shamsi Xiao-Yu Guo Arash Mohammadi 0001 Hamid Alinejad-Rokny Dino Sejdinovic Damith C. Ranasinghe Ehsan Abbasnejad Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks. 2024 abs/2407.20891 CoRR https://doi.org/10.48550/arXiv.2407.20891 db/journals/corr/corr2407.html#abs-2407-20891 streams/journals/corr

Siu Lun Chau Antonin Schrab Arthur Gretton Dino Sejdinovic Krikamol Muandet Credal Two-Sample Tests of Epistemic Ignorance. 2024 abs/2410.12921 CoRR https://doi.org/10.48550/arXiv.2410.12921 db/journals/corr/corr2410.html#abs-2410-12921 streams/journals/corr

Dino Sejdinovic An Overview of Causal Inference using Kernel Embeddings. 2024 abs/2410.22754 CoRR https://doi.org/10.48550/arXiv.2410.22754 db/journals/corr/corr2410.html#abs-2410-22754 streams/journals/corr

Daokun Zhang Russell Tsuchida Dino Sejdinovic Label Distribution Learning using the Squared Neural Family on the Probability Simplex. 2024 abs/2412.07324 CoRR https://doi.org/10.48550/arXiv.2412.07324 db/journals/corr/corr2412.html#abs-2412-07324 streams/journals/corr

Mengyan Zhang 0001 Shahine Bouabid Cheng Soon Ong Seth R. Flaxman Dino Sejdinovic Indirect Query Bayesian Optimization with Integrated Feedback. 2024 abs/2412.13559 CoRR https://doi.org/10.48550/arXiv.2412.13559 db/journals/corr/corr2412.html#abs-2412-13559 streams/journals/corr

Jonas Schuff Dominic T. Lennon Simon Geyer David L. Craig Federico Fedele Florian Vigneau Leon C. Camenzind Andreas V. Kuhlmann G. Andrew D. Briggs Dominik M. Zumbühl Dino Sejdinovic Natalia Ares Identifying Pauli spin blockade using deep learning. 1077 2023 August 7 Quantum https://doi.org/10.22331/q-2023-08-08-1077 db/journals/quantum/quantum7.html#SchuffLGCFVCKBZSA23

Adrian Perez-Suay Paula Gordaliza Jean-Michel Loubes Dino Sejdinovic Gustau Camps-Valls Fair Kernel Regression through Cross-Covariance Operators. 2023 2023 Trans. Mach. Learn. Res. https://openreview.net/forum?id=MyQ1e1VQQ3 db/journals/tmlr/tmlr2023.html#Perez-SuayGLSC23

Shahine Bouabid Jake Fawkes Dino Sejdinovic Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. 2885-2913 2023 ICML https://proceedings.mlr.press/v202/bouabid23a.html conf/icml/2023 db/conf/icml/icml2023.html#BouabidFS23 Siu Lun Chau Krikamol Muandet Dino Sejdinovic Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. 2023 NeurIPS http://papers.nips.cc/paper_files/paper/2023/hash/9f0b1220028dfa2ee82ca0a0e0fc52d1-Abstract-Conference.html conf/nips/2023 db/conf/nips/neurips2023.html#ChauMS23 Russell Tsuchida Cheng Soon Ong Dino Sejdinovic Squared Neural Families: A New Class of Tractable Density Models. 2023 NeurIPS http://papers.nips.cc/paper_files/paper/2023/hash/ea13534ee239bb3977795b8cc855bacc-Abstract-Conference.html conf/nips/2023 db/conf/nips/neurips2023.html#TsuchidaOS23 Veit David Wild Sahra Ghalebikesabi Dino Sejdinovic Jeremias Knoblauch A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. 2023 NeurIPS http://papers.nips.cc/paper_files/paper/2023/hash/7d25b1db211d99d5750ec45d65fd6e4e-Abstract-Conference.html conf/nips/2023 db/conf/nips/neurips2023.html#WildGSK23

Shahine Bouabid Jake Fawkes Dino Sejdinovic Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. 2023 abs/2301.11214 CoRR https://doi.org/10.48550/arXiv.2301.11214 db/journals/corr/corr2301.html#abs-2301-11214

Russell Tsuchida Cheng Soon Ong Dino Sejdinovic Squared Neural Families: A New Class of Tractable Density Models. 2023 abs/2305.13552 CoRR https://doi.org/10.48550/arXiv.2305.13552 db/journals/corr/corr2305.html#abs-2305-13552

Veit David Wild Sahra Ghalebikesabi Dino Sejdinovic Jeremias Knoblauch A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. 2023 abs/2305.15027 CoRR https://doi.org/10.48550/arXiv.2305.15027 db/journals/corr/corr2305.html#abs-2305-15027

Siu Lun Chau Krikamol Muandet Dino Sejdinovic Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. 2023 abs/2305.15167 CoRR https://doi.org/10.48550/arXiv.2305.15167 db/journals/corr/corr2305.html#abs-2305-15167

Qinyi Zhang 0002 Veit Wild Sarah Filippi Seth R. Flaxman Dino Sejdinovic Bayesian Kernel Two-Sample Testing. 1164-1176 2022 October 31 J. Comput. Graph. Stat. 4 https://doi.org/10.1080/10618600.2022.2067547 db/journals/jcgs/jcgs31.html#ZhangWFFS22

Robert Hu Geoff K. Nicholls Dino Sejdinovic Large scale tensor regression using kernels and variational inference. 2663-2713 2022 111 Mach. Learn. 7 https://doi.org/10.1007/s10994-021-06067-7 db/journals/ml/ml111.html#HuNS22

Zhu Li Adrián Pérez-Suay Gustau Camps-Valls Dino Sejdinovic Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. 108922 2022 132 Pattern Recognit. https://doi.org/10.1016/j.patcog.2022.108922 https://www.wikidata.org/entity/Q114142984 db/journals/pr/pr132.html#LiPCS22

David Rindt Robert Hu David Steinsaltz Dino Sejdinovic Survival regression with proper scoring rules and monotonic neural networks. 1190-1205 2022 AISTATS https://proceedings.mlr.press/v151/rindt22a.html conf/aistats/2022 db/conf/aistats/aistats2022.html#RindtHSS22 Siu Lun Chau Javier González 0002 Dino Sejdinovic Learning Inconsistent Preferences with Gaussian Processes. 2266-2281 2022 AISTATS https://proceedings.mlr.press/v151/lun-chau22a.html conf/aistats/2022 db/conf/aistats/aistats2022.html#ChauGS22 Jake Fawkes Robin J. Evans 0002 Dino Sejdinovic Selection, Ignorability and Challenges With Causal Fairness. 275-289 2022 CLeaR https://proceedings.mlr.press/v177/fawkes22a.html conf/clear2/2022 db/conf/clear2/clear2022.html#FawkesES22 Siu Lun Chau Robert Hu Javier González 0002 Dino Sejdinovic RKHS-SHAP: Shapley Values for Kernel Methods. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/54bb63eaec676b87a2278a22b1bd02a2-Abstract-Conference.html db/conf/nips/neurips2022.html#ChauHGS22 Robert Hu Siu Lun Chau Jaime Ferrando Huertas Dino Sejdinovic Explaining Preferences with Shapley Values. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/b1656d20067ca7c84a33785c4083a75e-Abstract-Conference.html db/conf/nips/neurips2022.html#HuCHS22 Robert Hu Siu Lun Chau Dino Sejdinovic Joan Glaunès Giga-scale Kernel Matrix-Vector Multiplication on GPU. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/3b1f32693e9fe15c949a0742bf226803-Abstract-Conference.html db/conf/nips/neurips2022.html#HuCSG22 Veit D. Wild Robert Hu Dino Sejdinovic Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. 2022 conf/nips/2022 NeurIPS http://papers.nips.cc/paper_files/paper/2022/hash/18210aa6209b9adfc97b8c17c3741d95-Abstract-Conference.html db/conf/nips/neurips2022.html#WildHS22 Siu Lun Chau Mihai Cucuringu Dino Sejdinovic Spectral Ranking with Covariates. 70-86 2022 ECML/PKDD (5) https://doi.org/10.1007/978-3-031-26419-1_5 conf/pkdd/2022-5 db/conf/pkdd/pkdd2022-5.html#ChauCS22

Jonas Schuff Dominic T. Lennon Simon Geyer David L. Craig Federico Fedele Florian Vigneau Leon C. Camenzind Andreas V. Kuhlmann G. Andrew D. Briggs Dominik M. Zumbühl Dino Sejdinovic Natalia Ares Identifying Pauli spin blockade using deep learning. 2022 abs/2202.00574 CoRR https://arxiv.org/abs/2202.00574 db/journals/corr/corr2202.html#abs-2202-00574

Robert Hu Dino Sejdinovic Joan Alexis Glaunès Giga-scale Kernel Matrix Vector Multiplication on GPU. 2022 abs/2202.01085 CoRR https://arxiv.org/abs/2202.01085 db/journals/corr/corr2202.html#abs-2202-01085

Jake Fawkes Robin J. Evans 0002 Dino Sejdinovic Selection, Ignorability and Challenges With Causal Fairness. 2022 abs/2202.13774 CoRR https://arxiv.org/abs/2202.13774 db/journals/corr/corr2202.html#abs-2202-13774

Veit D. Wild Robert Hu Dino Sejdinovic Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. 2022 abs/2205.06342 CoRR https://doi.org/10.48550/arXiv.2205.06342 db/journals/corr/corr2205.html#abs-2205-06342

Robert Hu Siu Lun Chau Jaime Ferrando Huertas Dino Sejdinovic Explaining Preferences with Shapley Values. 2022 abs/2205.13662 CoRR https://doi.org/10.48550/arXiv.2205.13662 db/journals/corr/corr2205.html#abs-2205-13662

Antonin Schrab Wittawat Jitkrittum Zoltán Szabó 0001 Dino Sejdinovic Arthur Gretton Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. 2022 abs/2206.11142 CoRR https://doi.org/10.48550/arXiv.2206.11142 db/journals/corr/corr2206.html#abs-2206-11142

Diego Martinez-Taboada Dino Sejdinovic Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. 2022 abs/2210.13692 CoRR https://doi.org/10.48550/arXiv.2210.13692 db/journals/corr/corr2210.html#abs-2210-13692

Diego Martinez-Taboada Dino Sejdinovic Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. 2022 abs/2211.01518 CoRR https://doi.org/10.48550/arXiv.2211.01518 db/journals/corr/corr2211.html#abs-2211-01518

Jake Fawkes Robert Hu Robin J. Evans 0002 Dino Sejdinovic Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. 2022 abs/2212.04922 CoRR https://doi.org/10.48550/arXiv.2212.04922 db/journals/corr/corr2212.html#abs-2212-04922

Zhu Li Jean-Francois Ton Dino Oglic Dino Sejdinovic Towards a Unified Analysis of Random Fourier Features. 108:1-108:51 2021 22 J. Mach. Learn. Res. https://jmlr.org/papers/v22/20-1369.html db/journals/jmlr/jmlr22.html#LiTOS21

Valerie C. Bradley Shiro Kuriwaki Michael Isakov Dino Sejdinovic Xiao-Li Meng Seth R. Flaxman Unrepresentative big surveys significantly overestimated US vaccine uptake. 695-700 2021 600 Nat. 7890 https://doi.org/10.1038/s41586-021-04198-4 db/journals/nature/nature600.html#BradleyKISMF21

Gordon S. Blair Richard Bassett Lucy Bastin Lindsay Beevers Maribel Isabel Borrajo Mike Brown Sarah L. Dance Ada Dionescu Liz Edwards Maria Angela Ferrario Rob Fraser Harriet Fraser Simon Gardner Peter A. Henrys Tony Hey Stuart Homann Chantal Huijbers James Hutchison Phil Jonathan Rob Lamb Sophie Laurie Amber Leeson David Leslie Malcolm McMillan Vatsala Nundloll Oluwole K. Oyebamiji Jordan Phillipson Vicky Pope Rachel Prudden Stefan Reis Maria Salama Faiza Samreen Dino Sejdinovic Will Simm Roger Street Lauren Thornton Ross Towe Joshua Vande Hey Massimo Vieno Joanne A. Waller John Watkins The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. 100156 2021 2 Patterns 1 https://doi.org/10.1016/j.patter.2020.100156 db/journals/patterns/patterns2.html#BlairBBBBBDDEFF21

Xingyue Pu Siu Lun Chau Xiaowen Dong 0001 Dino Sejdinovic Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. 192-207 2021 7 IEEE Trans. Signal Inf. Process. over Networks https://doi.org/10.1109/TSIPN.2021.3059995 db/journals/tsipn/tsipn7.html#PuCDS21

Jean-François Ton Dino Sejdinovic Kenji Fukumizu Meta Learning for Causal Direction. 9897-9905 2021 AAAI https://doi.org/10.1609/aaai.v35i11.17189 conf/aaai/2021 db/conf/aaai/aaai2021.html#TonSF21 Jean-Francois Ton Lucian Chan Yee Whye Teh Dino Sejdinovic Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. 1099-1107 2021 AISTATS http://proceedings.mlr.press/v130/ton21a.html conf/aistats/2021 db/conf/aistats/aistats2021.html#TonCTS21 Siu Lun Chau Jean-Francois Ton Javier González 0002 Yee Whye Teh Dino Sejdinovic BayesIMP: Uncertainty Quantification for Causal Data Fusion. 3466-3477 2021 NeurIPS https://proceedings.neurips.cc/paper/2021/hash/1ca5c750a30312d1919ae6a4d636dcc4-Abstract.html conf/nips/2021 db/conf/nips/neurips2021.html#ChauTGTS21 Siu Lun Chau Shahine Bouabid Dino Sejdinovic Deconditional Downscaling with Gaussian Processes. 17813-17825 2021 NeurIPS https://proceedings.neurips.cc/paper/2021/hash/94aef38441efa3380a3bed3faf1f9d5d-Abstract.html conf/nips/2021 db/conf/nips/neurips2021.html#ChauBS21 Robert Hu Dino Sejdinovic Robust Deep Interpretable Features for Binary Image Classification. 2021 NLDL https://doi.org/10.7557/18.5708 conf/nldl/2021 db/conf/nldl/nldl2021.html#HuS21 Anthony L. Caterini Robert Cornish Dino Sejdinovic Arnaud Doucet Variational inference with continuously-indexed normalizing flows. 44-53 2021 UAI https://proceedings.mlr.press/v161/caterini21a.html conf/uai/2021 db/conf/uai/uai2021.html#CateriniCSD21

David Rindt Robert Hu David Steinsaltz Dino Sejdinovic Time-to-event regression using partially monotonic neural networks. 2021 abs/2103.14755 CoRR https://arxiv.org/abs/2103.14755 db/journals/corr/corr2103.html#abs-2103-14755

Siu Lun Chau Shahine Bouabid Dino Sejdinovic Deconditional Downscaling with Gaussian Processes. 2021 abs/2105.12909 CoRR https://arxiv.org/abs/2105.12909 db/journals/corr/corr2105.html#abs-2105-12909

Veit Wild Motonobu Kanagawa Dino Sejdinovic Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. 2021 abs/2106.01121 CoRR https://arxiv.org/abs/2106.01121 db/journals/corr/corr2106.html#abs-2106-01121

Siu Lun Chau Jean-François Ton Javier González 0002 Yee Whye Teh Dino Sejdinovic BayesIMP: Uncertainty Quantification for Causal Data Fusion. 2021 abs/2106.03477 CoRR https://arxiv.org/abs/2106.03477 db/journals/corr/corr2106.html#abs-2106-03477

Brandon Severin Dominic T. Lennon Leon C. Camenzind Florian Vigneau Federico Fedele D. Jirovec A. Ballabio Daniel Chrastina Giovanni Isella M. de Kruijf Miguel J. Carballido Simon Svab Andreas V. Kuhlmann F. R. Braakman Simon Geyer F. N. M. Froning H. Moon Michael A. Osborne Dino Sejdinovic G. Katsaros Dominik M. Zumbühl G. Andrew D. Briggs Natalia Ares Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. 2021 abs/2107.12975 CoRR https://arxiv.org/abs/2107.12975 db/journals/corr/corr2107.html#abs-2107-12975

Siu Lun Chau Javier González 0002 Dino Sejdinovic RKHS-SHAP: Shapley Values for Kernel Methods. 2021 abs/2110.09167 CoRR https://arxiv.org/abs/2110.09167 db/journals/corr/corr2110.html#abs-2110-09167

David L. Craig H. Moon Federico Fedele Dominic T. Lennon Barnaby van Straaten Florian Vigneau Leon C. Camenzind Dominik M. Zumbühl G. Andrew D. Briggs Michael A. Osborne Dino Sejdinovic Natalia Ares Bridging the reality gap in quantum devices with physics-aware machine learning. 2021 abs/2111.11285 CoRR https://arxiv.org/abs/2111.11285 db/journals/corr/corr2111.html#abs-2111-11285

Tim G. J. Rudner Dino Sejdinovic Yarin Gal Inter-domain Deep Gaussian Processes. 8286-8294 2020 ICML http://proceedings.mlr.press/v119/rudner20a.html conf/icml/2020 db/conf/icml/icml2020.html#RudnerSG20

Hyungil Moon Dominic T. Lennon James Kirkpatrick Nina M. van Esbroeck Leon C. Camenzind Liuqi Yu Florian Vigneau Dominik M. Zumbühl G. Andrew D. Briggs Michael A. Osborne Dino Sejdinovic Edward A. Laird Natalia Ares Machine learning enables completely automatic tuning of a quantum device faster than human experts. 2020 abs/2001.02589 CoRR http://arxiv.org/abs/2001.02589 db/journals/corr/corr2001.html#abs-2001-02589

Nina M. van Esbroeck Dominic T. Lennon Hyungil Moon V. Nguyen Florian Vigneau Leon C. Camenzind Liuqi Yu Dominik M. Zumbühl G. Andrew D. Briggs Dino Sejdinovic Natalia Ares Quantum device fine-tuning using unsupervised embedding learning. 2020 abs/2001.04409 CoRR https://arxiv.org/abs/2001.04409 db/journals/corr/corr2001.html#abs-2001-04409

Robert Hu Geoff K. Nicholls Dino Sejdinovic Large Scale Tensor Regression using Kernels and Variational Inference. 2020 abs/2002.04704 CoRR https://arxiv.org/abs/2002.04704 db/journals/corr/corr2002.html#abs-2002-04704

Siu Lun Chau Mihai Cucuringu Dino Sejdinovic Spectral Ranking with Covariates. 2020 abs/2005.04035 CoRR https://arxiv.org/abs/2005.04035 db/journals/corr/corr2005.html#abs-2005-04035

Siu Lun Chau Javier González 0002 Dino Sejdinovic Learning Inconsistent Preferences with Kernel Methods. 2020 abs/2006.03847 CoRR https://arxiv.org/abs/2006.03847 db/journals/corr/corr2006.html#abs-2006-03847

Gustau Camps-Valls Dino Sejdinovic Jakob Runge Markus Reichstein A Perspective on Gaussian Processes for Earth Observation. 2020 abs/2007.01238 CoRR https://arxiv.org/abs/2007.01238 db/journals/corr/corr2007.html#abs-2007-01238

Jean-Francois Ton Dino Sejdinovic Kenji Fukumizu Meta Learning for Causal Direction. 2020 abs/2007.02809 CoRR https://arxiv.org/abs/2007.02809 db/journals/corr/corr2007.html#abs-2007-02809

Anthony L. Caterini Robert Cornish Dino Sejdinovic Arnaud Doucet Variational Inference with Continuously-Indexed Normalizing Flows. 2020 abs/2007.05426 CoRR https://arxiv.org/abs/2007.05426 db/journals/corr/corr2007.html#abs-2007-05426

Zhu Li Weijie J. Su Dino Sejdinovic Benign Overfitting and Noisy Features. 2020 abs/2008.02901 CoRR https://arxiv.org/abs/2008.02901 db/journals/corr/corr2008.html#abs-2008-02901

Xingyue Pu Siu Lun Chau Xiaowen Dong 0001 Dino Sejdinovic Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. 2020 abs/2008.10065 CoRR https://arxiv.org/abs/2008.10065 db/journals/corr/corr2008.html#abs-2008-10065

V. Nguyen S. B. Orbell Dominic T. Lennon Hyungil Moon Florian Vigneau Leon C. Camenzind Liuqi Yu Dominik M. Zumbühl G. Andrew D. Briggs Michael A. Osborne Dino Sejdinovic Natalia Ares Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. 2020 abs/2009.14825 CoRR https://arxiv.org/abs/2009.14825 db/journals/corr/corr2009.html#abs-2009-14825

Tim G. J. Rudner Dino Sejdinovic Yarin Gal Inter-domain Deep Gaussian Processes. 2020 abs/2011.00415 CoRR https://arxiv.org/abs/2011.00415 db/journals/corr/corr2011.html#abs-2011-00415

Zhu Li Jean-Francois Ton Dino Oglic Dino Sejdinovic Towards a Unified Analysis of Random Fourier Features. 3905-3914 2019 ICML http://proceedings.mlr.press/v97/li19k.html conf/icml/2019 db/conf/icml/icml2019.html#LiTOS19 Ho Chung Leon Law Peilin Zhao Leung Sing Chan Junzhou Huang Dino Sejdinovic Hyperparameter Learning via Distributional Transfer. 6801-6812 2019 NeurIPS https://proceedings.neurips.cc/paper/2019/hash/6754e06e46dfa419d5afe3c9781cecad-Abstract.html http://papers.nips.cc/paper/8905-hyperparameter-learning-via-distributional-transfer conf/nips/2019 db/conf/nips/nips2019.html#LawZCHS19 Anant Raj Ho Chung Leon Law Dino Sejdinovic Mijung Park A Differentially Private Kernel Two-Sample Test. 697-724 2019 ECML/PKDD (1) https://doi.org/10.1007/978-3-030-46150-8_41 conf/pkdd/2019-1 db/conf/pkdd/pkdd2019-1.html#RajLSP19

Jean-Francois Ton Lucian Chan Yee Whye Teh Dino Sejdinovic Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. 2019 abs/1906.02236 CoRR http://arxiv.org/abs/1906.02236 db/journals/corr/corr1906.html#abs-1906-02236

Zhu Li Adrian Perez-Suay Gustau Camps-Valls Dino Sejdinovic Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. 2019 abs/1911.04322 CoRR http://arxiv.org/abs/1911.04322 db/journals/corr/corr1911.html#abs-1911-04322

Duncan Watson-Parris Samuel Sutherland Matthew Christensen Anthony L. Caterini Dino Sejdinovic Philip Stier Detecting anthropogenic cloud perturbations with deep learning. 2019 abs/1911.13061 CoRR http://arxiv.org/abs/1911.13061 db/journals/corr/corr1911.html#abs-1911-13061

Qinyi Zhang 0002 Sarah Filippi Arthur Gretton Dino Sejdinovic Large-scale kernel methods for independence testing. 113-130 2018 28 Stat. Comput. 1 https://doi.org/10.1007/s11222-016-9721-7 db/journals/sac/sac28.html#ZhangFGS18

Ho Chung Leon Law Danica J. Sutherland Dino Sejdinovic Seth R. Flaxman Bayesian Approaches to Distribution Regression. 1167-1176 2018 AISTATS http://proceedings.mlr.press/v84/law18a.html conf/aistats/2018 db/conf/aistats/aistats2018.html#LawSSF18 Ho Chung Leon Law Dino Sejdinovic Ewan Cameron Tim C. D. Lucas Seth R. Flaxman Katherine Battle Kenji Fukumizu Variational Learning on Aggregate Outputs with Gaussian Processes. 6084-6094 2018 NeurIPS https://proceedings.neurips.cc/paper/2018/hash/24b43fb034a10d78bec71274033b4096-Abstract.html http://papers.nips.cc/paper/7847-variational-learning-on-aggregate-outputs-with-gaussian-processes conf/nips/2018 db/conf/nips/nips2018.html#LawSCLFBF18 Jovana Mitrovic Dino Sejdinovic Yee Whye Teh Causal Inference via Kernel Deviance Measures. 6986-6994 2018 NeurIPS https://proceedings.neurips.cc/paper/2018/hash/73fed7fd472e502d8908794430511f4d-Abstract.html http://papers.nips.cc/paper/7930-causal-inference-via-kernel-deviance-measures conf/nips/2018 db/conf/nips/nips2018.html#MitrovicST18 Anthony L. Caterini Arnaud Doucet Dino Sejdinovic Hamiltonian Variational Auto-Encoder. 8178-8188 2018 NeurIPS https://proceedings.neurips.cc/paper/2018/hash/3202111cf90e7c816a472aaceb72b0df-Abstract.html http://papers.nips.cc/paper/8039-hamiltonian-variational-auto-encoder conf/nips/2018 db/conf/nips/nips2018.html#CateriniDS18

Jovana Mitrovic Dino Sejdinovic Yee Whye Teh Causal Inference via Kernel Deviance Measures. 2018 abs/1804.04622 CoRR http://arxiv.org/abs/1804.04622 db/journals/corr/corr1804.html#abs-1804-04622

Ho Chung Leon Law Dino Sejdinovic Ewan Cameron Tim C. D. Lucas Seth R. Flaxman Katherine Battle Kenji Fukumizu Variational Learning on Aggregate Outputs with Gaussian Processes. 2018 abs/1805.08463 CoRR http://arxiv.org/abs/1805.08463 db/journals/corr/corr1805.html#abs-1805-08463

Anthony L. Caterini Arnaud Doucet Dino Sejdinovic Hamiltonian Variational Auto-Encoder. 2018 abs/1805.11328 CoRR http://arxiv.org/abs/1805.11328 db/journals/corr/corr1805.html#abs-1805-11328

Zhu Li Jean-Francois Ton Dino Oglic Dino Sejdinovic A Unified Analysis of Random Fourier Features. 2018 abs/1806.09178 CoRR http://arxiv.org/abs/1806.09178 db/journals/corr/corr1806.html#abs-1806-09178

Motonobu Kanagawa Philipp Hennig Dino Sejdinovic Bharath K. Sriperumbudur Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. 2018 abs/1807.02582 CoRR http://arxiv.org/abs/1807.02582 db/journals/corr/corr1807.html#abs-1807-02582

Anant Raj Ho Chung Leon Law Dino Sejdinovic Mijung Park A Differentially Private Kernel Two-Sample Test. 2018 abs/1808.00380 CoRR http://arxiv.org/abs/1808.00380 db/journals/corr/corr1808.html#abs-1808-00380

Ho Chung Leon Law Peilin Zhao Junzhou Huang Dino Sejdinovic Hyperparameter Learning via Distributional Transfer. 2018 abs/1810.06305 CoRR http://arxiv.org/abs/1810.06305 db/journals/corr/corr1810.html#abs-1810-06305

François-Xavier Briol Chris J. Oates Mark A. Girolami Michael A. Osborne Dino Sejdinovic Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". 2018 abs/1811.10275 CoRR http://arxiv.org/abs/1811.10275 db/journals/corr/corr1811.html#abs-1811-10275

Seth R. Flaxman Yee Whye Teh Dino Sejdinovic Poisson intensity estimation with reproducing kernels. 270-279 2017 AISTATS http://proceedings.mlr.press/v54/flaxman17a.html conf/aistats/2017 db/conf/aistats/aistats2017.html#FlaxmanTS17 Jovana Mitrovic Dino Sejdinovic Yee Whye Teh Deep Kernel Machines via the Kernel Reparametrization Trick. 2017 ICLR (Workshop) https://openreview.net/forum?id=Bkiqt3Ntg conf/iclr/2017w db/conf/iclr/iclr2017w.html#MitrovicST17 Ho Chung Leon Law Christopher Yau Dino Sejdinovic Testing and Learning on Distributions with Symmetric Noise Invariance. 1343-1353 2017 NIPS https://proceedings.neurips.cc/paper/2017/hash/67d16d00201083a2b118dd5128dd6f59-Abstract.html http://papers.nips.cc/paper/6733-testing-and-learning-on-distributions-with-symmetric-noise-invariance conf/nips/2017 db/conf/nips/nips2017.html#LawYS17 Ingmar Schuster Heiko Strathmann Brooks Paige Dino Sejdinovic Kernel Sequential Monte Carlo. 390-409 2017 ECML/PKDD (1) https://doi.org/10.1007/978-3-319-71249-9_24 conf/pkdd/2017-1 db/conf/pkdd/pkdd2017-1.html#SchusterSPS17 Qinyi Zhang 0002 Sarah Filippi Seth R. Flaxman Dino Sejdinovic Feature-to-Feature Regression for a Two-Step Conditional Independence Test. 2017 UAI http://auai.org/uai2017/proceedings/papers/250.pdf conf/uai/2017 db/conf/uai/uai2017.html#ZhangFFS17

Ho Chung Leon Law Danica J. Sutherland Dino Sejdinovic Seth R. Flaxman Bayesian Distribution Regression. 2017 abs/1705.04293 CoRR http://arxiv.org/abs/1705.04293 db/journals/corr/corr1705.html#LawSSF17

Dejan Vukobratovic Dusan Jakovetic Vitaly Skachek Dragana Bajovic Dino Sejdinovic Günes Karabulut-Kurt Camilla Hollanti Ingo Fischer CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. 3360-3378 2016 4 IEEE Access https://doi.org/10.1109/ACCESS.2016.2585468 https://www.wikidata.org/entity/Q62700091 db/journals/access/access4.html#VukobratovicJSB16

Mijung Park Wittawat Jitkrittum Dino Sejdinovic K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. 398-407 2016 AISTATS http://proceedings.mlr.press/v51/park16.html conf/aistats/2016 db/conf/aistats/aistats2016.html#ParkJS16 Gianni Franchi Jesús Angulo Dino Sejdinovic Hyperspectral image classification with support vector machines on kernel distribution embeddings. 1898-1902 2016 ICIP https://doi.org/10.1109/ICIP.2016.7532688 conf/icip/2016 db/conf/icip/icip2016.html#FranchiAS16 Jovana Mitrovic Dino Sejdinovic Yee Whye Teh DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. 1482-1491 2016 ICML http://proceedings.mlr.press/v48/mitrovic16.html conf/icml/2016 db/conf/icml/icml2016.html#MitrovicST16 Dejan Vukobratovic Dusan Jakovetic Vitaly Skachek Dragana Bajovic Dino Sejdinovic Network function computation as a service in future 5G machine type communications. 365-369 2016 ISTC https://doi.org/10.1109/ISTC.2016.7593138 conf/istc/2016 db/conf/istc/istc2016.html#VukobratovicJSB16 Seth R. Flaxman Dino Sejdinovic John P. Cunningham Sarah Filippi Bayesian Learning of Kernel Embeddings. 2016 UAI http://auai.org/uai2016/proceedings/papers/145.pdf conf/uai/2016 db/conf/uai/uai2016.html#FlaxmanSCF16 Brooks Paige Dino Sejdinovic Frank D. Wood Super-Sampling with a Reservoir. 2016 UAI http://auai.org/uai2016/proceedings/papers/293.pdf conf/uai/2016 db/conf/uai/uai2016.html#PaigeSW16

Jovana Mitrovic Dino Sejdinovic Yee Whye Teh DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. 2016 abs/1602.04805 CoRR http://arxiv.org/abs/1602.04805 db/journals/corr/corr1602.html#MitrovicST16

Gianni Franchi Jesús Angulo Dino Sejdinovic Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. 2016 abs/1605.09136 CoRR http://arxiv.org/abs/1605.09136 db/journals/corr/corr1605.html#FranchiAS16

Dejan Vukobratovic Dusan Jakovetic Vitaly Skachek Dragana Bajovic Dino Sejdinovic Gunes Karabulut-Kurt Camilla Hollanti Ingo Fischer CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. 2016 abs/1609.03363 CoRR http://arxiv.org/abs/1609.03363 db/journals/corr/corr1609.html#VukobratovicJSB16

Heiko Strathmann Dino Sejdinovic Samuel Livingstone Zoltán Szabó 0001 Arthur Gretton Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. 955-963 2015 NIPS https://proceedings.neurips.cc/paper/2015/hash/8ebda540cbcc4d7336496819a46a1b68-Abstract.html http://papers.nips.cc/paper/5890-gradient-free-hamiltonian-monte-carlo-with-efficient-kernel-exponential-families conf/nips/2015 db/conf/nips/nips2015.html#StrathmannSLSG15 Kacper Chwialkowski Aaditya Ramdas Dino Sejdinovic Arthur Gretton Fast Two-Sample Testing with Analytic Representations of Probability Measures. 1981-1989 2015 NIPS https://proceedings.neurips.cc/paper/2015/hash/b571ecea16a9824023ee1af16897a582-Abstract.html http://papers.nips.cc/paper/5685-fast-two-sample-testing-with-analytic-representations-of-probability-measures conf/nips/2015 db/conf/nips/nips2015.html#ChwialkowskiRSG15 Dejan Vukobratovic Dino Sejdinovic Aleksandra Pizurica Compressed Sensing using sparse binary measurements: A rateless coding perspective. 86-90 2015 SPAWC https://doi.org/10.1109/SPAWC.2015.7227005 conf/spawc/2015 db/conf/spawc/spawc2015.html#VukobratovicSP15 Wittawat Jitkrittum Arthur Gretton Nicolas Heess S. M. Ali Eslami Balaji Lakshminarayanan Dino Sejdinovic Zoltán Szabó 0001 Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. 405-414 2015 UAI conf/uai/2015 db/conf/uai/uai2015.html#JitkrittumGHELS15 http://auai.org/uai2015/proceedings/papers/235.pdf

Heiko Strathmann Dino Sejdinovic Mark A. Girolami Unbiased Bayes for Big Data: Paths of Partial Posteriors. 2015 abs/1501.03326 CoRR http://arxiv.org/abs/1501.03326 db/journals/corr/corr1501.html#StrathmannSG15

Mijung Park Wittawat Jitkrittum Dino Sejdinovic K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. 2015 abs/1502.02558 CoRR http://arxiv.org/abs/1502.02558 db/journals/corr/corr1502.html#ParkJS15

Wittawat Jitkrittum Arthur Gretton Nicolas Heess S. M. Ali Eslami Balaji Lakshminarayanan Dino Sejdinovic Zoltán Szabó 0001 Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. 2015 abs/1503.02551 CoRR http://arxiv.org/abs/1503.02551 db/journals/corr/corr1503.html#JitkrittumGHELS15

François-Xavier Briol Chris J. Oates Mark A. Girolami Michael A. Osborne Dino Sejdinovic Probabilistic Integration. 2015 abs/1512.00933 CoRR http://arxiv.org/abs/1512.00933 db/journals/corr/corr1512.html#BriolOGOS15

Dino Sejdinovic Heiko Strathmann Maria Lomeli Garcia Christophe Andrieu Arthur Gretton Kernel Adaptive Metropolis-Hastings. 1665-1673 2014 ICML http://proceedings.mlr.press/v32/sejdinovic14.html conf/icml/2014 db/conf/icml/icml2014.html#SejdinovicSGAG14 Kacper Chwialkowski Dino Sejdinovic Arthur Gretton A Wild Bootstrap for Degenerate Kernel Tests. 3608-3616 2014 NIPS https://proceedings.neurips.cc/paper/2014/hash/13168e6a2e6c84b4b7de9390c0ef5ec5-Abstract.html http://papers.nips.cc/paper/5452-a-wild-bootstrap-for-degenerate-kernel-tests conf/nips/2014 db/conf/nips/nips2014.html#ChwialkowskiSG14 Dino Sejdinovic Arthur Gretton Wicher Bergsma A Kernel Test for Three-Variable Interactions. 1124-1132 2013 NIPS https://proceedings.neurips.cc/paper/2013/hash/076a0c97d09cf1a0ec3e19c7f2529f2b-Abstract.html http://papers.nips.cc/paper/4893-a-kernel-test-for-three-variable-interactions conf/nips/2013 db/conf/nips/nips2013.html#SejdinovicGB13

Dino Sejdinovic Maria Lomeli Garcia Heiko Strathmann Christophe Andrieu Arthur Gretton Kernel Adaptive Metropolis-Hastings. 2013 CoRR http://arxiv.org/abs/1307.5302 abs/1307.5302 db/journals/corr/corr1307.html#SejdinovicGSAG13

Andreas Müller 0008 Dino Sejdinovic Robert J. Piechocki Approximate message passing under finite alphabet constraints. 3177-3180 2012 ICASSP https://doi.org/10.1109/ICASSP.2012.6288590 conf/icassp/2012 db/conf/icassp/icassp2012.html#MullerSP12 Robert J. Piechocki Dino Sejdinovic Combinatorial channel signature modulation for wireless ad-hoc networks. 4684-4689 2012 ICC https://doi.org/10.1109/ICC.2012.6363956 conf/icc/2012 db/conf/icc/icc2012.html#PiechockiS12 Dino Sejdinovic Arthur Gretton Bharath K. Sriperumbudur Kenji Fukumizu Hypothesis testing using pairwise distances and associated kernels. 2012 ICML http://icml.cc/2012/papers/575.pdf conf/icml/2012 db/conf/icml/icml2012.html#SejdinovicGSF12 Arthur Gretton Bharath K. Sriperumbudur Dino Sejdinovic Heiko Strathmann Sivaraman Balakrishnan Massimiliano Pontil Kenji Fukumizu Optimal kernel choice for large-scale two-sample tests. 1214-1222 2012 NIPS https://proceedings.neurips.cc/paper/2012/hash/dbe272bab69f8e13f14b405e038deb64-Abstract.html http://papers.nips.cc/paper/4727-optimal-kernel-choice-for-large-scale-two-sample-tests conf/nips/2012 db/conf/nips/nips2012.html#GrettonSSSBPF12

Andreas Müller 0008 Dino Sejdinovic Robert J. Piechocki Approximate Message Passing under Finite Alphabet Constraints http://arxiv.org/abs/1201.4949 2012 CoRR abs/1201.4949 db/journals/corr/corr1201.html#abs-1201-4949

Robert J. Piechocki Dino Sejdinovic Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks http://arxiv.org/abs/1201.5608 2012 CoRR abs/1201.5608 db/journals/corr/corr1201.html#abs-1201-5608

Dino Sejdinovic Arthur Gretton Bharath K. Sriperumbudur Kenji Fukumizu Hypothesis testing using pairwise distances and associated kernels (with Appendix) http://arxiv.org/abs/1205.0411 2012 CoRR abs/1205.0411 db/journals/corr/corr1205.html#abs-1205-0411

Dino Sejdinovic Bharath K. Sriperumbudur Arthur Gretton Kenji Fukumizu Equivalence of distance-based and RKHS-based statistics in hypothesis testing http://arxiv.org/abs/1207.6076 2012 CoRR abs/1207.6076 db/journals/corr/corr1207.html#abs-1207-6076

Oliver Johnson Dino Sejdinovic James R. Cruise Ayalvadi Ganesh 0001 Robert J. Piechocki Non-parametric change-point detection using string matching algorithms http://arxiv.org/abs/1106.5714 2011 CoRR abs/1106.5714 db/journals/corr/corr1106.html#abs-1106-5714

Dino Sejdinovic Robert J. Piechocki Angela Doufexi Mohamed Ismail Decentralised distributed fountain coding: asymptotic analysis and design. 42-44 2010 14 IEEE Commun. Lett. 1 https://doi.org/10.1109/LCOMM.2010.01.091541 db/journals/icl/icl14.html#SejdinovicPDI10

Dino Sejdinovic Oliver Johnson Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction. 998-1003 2010 Allerton https://doi.org/10.1109/ALLERTON.2010.5707018 conf/allerton/2010 db/conf/allerton/allerton2010.html#SejdinovicJ10 Dino Sejdinovic Christophe Andrieu Robert J. Piechocki Bayesian sequential compressed sensing in sparse dynamical systems. 1730-1736 2010 Allerton https://doi.org/10.1109/ALLERTON.2010.5707125 conf/allerton/2010 db/conf/allerton/allerton2010.html#SejdinovicAP10

Dino Sejdinovic Oliver Johnson Note on Noisy Group Testing: Asymptotic Bounds and Belief Propagation Reconstruction http://arxiv.org/abs/1010.2441 2010 CoRR abs/1010.2441 db/journals/corr/corr1010.html#abs-1010-2441

Dino Sejdinovic Dejan Vukobratovic Angela Doufexi Vojin Senk Robert J. Piechocki Expanding window fountain codes for unequal error protection. 2510-2516 2009 57 IEEE Trans. Commun. 9 https://doi.org/10.1109/TCOMM.2009.09.070616 db/journals/tcom/tcom57.html#SejdinovicVDSP09

Dejan Vukobratovic Vladimir Stankovic 0001 Dino Sejdinovic Lina Stankovic Zixiang Xiong Scalable Video Multicast Using Expanding Window Fountain Codes. 1094-1104 2009 11 IEEE Trans. Multim. 6 https://doi.org/10.1109/TMM.2009.2026087 db/journals/tmm/tmm11.html#VukobratovicSSSX09

Dino Sejdinovic Robert J. Piechocki Angela Doufexi Mohamed Ismail Fountain code design for data multicast with side information. 5155-5165 2009 8 IEEE Trans. Wirel. Commun. 10 https://doi.org/10.1109/TWC.2009.081076 db/journals/twc/twc8.html#SejdinovicPDI09

Dino Sejdinovic Robert J. Piechocki Angela Doufexi AND-OR tree analysis of distributed LT codes. 261-265 2009 ITW https://doi.org/10.1109/ITWNIT.2009.5158583 conf/itw/2009 db/conf/itw/itw2009.html#SejdinovicPD09 Dino Sejdinovic Robert J. Piechocki Angela Doufexi Rateless distributed source code design. 2009 MobiMedia https://doi.org/10.4108/ICST.MOBIMEDIA2009.7455 https://dl.acm.org/citation.cfm?id=1653578 conf/mobimedia/2009 db/conf/mobimedia/mobimedia2009.html#SejdinovicPD09 Dejan Vukobratovic Vladimir Stankovic 0001 Lina Stankovic Dino Sejdinovic Precoded EWF codes for unequal error protection of scalable video. 2009 MobiMedia https://doi.org/10.4108/ICST.MOBIMEDIA2009.7407 https://dl.acm.org/citation.cfm?id=1653559 conf/mobimedia/2009 db/conf/mobimedia/mobimedia2009.html#VukobratovicSSS09 Dino Sejdinovic Robert J. Piechocki Angela Doufexi Mohamed Ismail Rate Adaptive Binary Erasure Quantization with Dual Fountain Codes. 1203-1207 2008 GLOBECOM https://doi.org/10.1109/GLOCOM.2008.ECP.238 conf/globecom/2008 db/conf/globecom/globecom2008.html#SejdinovicPDI08 Dino Sejdinovic Robert J. Piechocki Angela Doufexi Mohamed Ismail Fountain Coding with Decoder Side Information. 4477-4482 2008 ICC https://doi.org/10.1109/ICC.2008.840 conf/icc/2008 db/conf/icc/icc2008.html#SejdinovicPDI08 Dejan Vukobratovic Vladimir Stankovic 0001 Dino Sejdinovic Lina Stankovic Zixiang Xiong Expanding Window Fountain codes for scalable video multicast. 77-80 2008 ICME https://doi.org/10.1109/ICME.2008.4607375 https://doi.ieeecomputersociety.org/10.1109/ICME.2008.4607375 conf/icmcs/2008 db/conf/icmcs/icme2008.html#VukobratovicSSSX08 Dino Sejdinovic Vishakan Ponnampalam Robert J. Piechocki Angela Doufexi The Throughput Analysis of Different IR-HARQ Schemes Based on Fountain Codes. 267-272 2008 WCNC https://doi.org/10.1109/WCNC.2008.52 conf/wcnc/2008 db/conf/wcnc/wcnc2008.html#SejdinovicPPD08 Ehsan Abbasnejad Hamid Alinejad-Rokny Christophe Andrieu Jesús Angulo Natalia Ares Dragana Bajovic Sivaraman Balakrishnan A. Ballabio Richard Bassett Lucy Bastin Katherine Battle Lindsay Beevers Luca Bergen Wicher Bergsma Gordon S. Blair Howard D. Bondell Edwin V. Bonilla Maria Isabel BorrajoMaribel Isabel Borrajo Shahine Bouabid F. R. Braakman Valerie C. Bradley G. Andrew D. Briggs François-Xavier Briol Mike Brown Leon C. Camenzind Ewan Cameron Gustau Camps-Valls Miguel J. Carballido Anthony L. Caterini Leung Sing Chan Lucian Chan Siu Lun Chau Tat-Jun Chin Daniel Chrastina Matthew Christensen Kacper Chwialkowski Robert Cornish David L. Craig James R. Cruise Mihai Cucuringu John P. Cunningham Sarah L. Dance Vanessa Didelez Ada Dionescu Bao Gia Doan Xiaowen Dong 0001 Arnaud Doucet Angela Doufexi Liz Edwards Nina M. van Esbroeck S. M. Ali Eslami Robin J. Evans 0002 Jake Fawkes Federico Fedele Maria Angela Ferrario Sarah Filippi Ingo Fischer Seth R. Flaxman Edwin Fong Gianni Franchi Harriet Fraser Rob Fraser F. N. M. Froning Kenji Fukumizu Yarin Gal Ayalvadi J. GaneshAyalvadi Ganesh 0001 Erdun Gao Simon Gardner Simon Geyer Sahra Ghalebikesabi Mark A. Girolami Joan GlaunèsJoan Alexis Glaunès Mingming Gong Javier González 0002 Paula Gordaliza Arthur Gretton Xiao-Yu Guo Nicolas Heess Philipp Hennig Peter A. Henrys Anthony J. G. HeyTony Hey Joshua Vande Hey Camilla Hollanti Stuart Homann David Howard Robert Hu Junzhou Huang Jaime Ferrando Huertas Chantal Huijbers James Hutchison Michael Isakov Giovanni Isella Mohamed Ismail Dusan Jakovetic D. Jirovec Wittawat Jitkrittum Oliver Johnson Philip JonathanPhil Jonathan Motonobu Kanagawa Gunes Karabulut-KurtGünes Karabulut-Kurt G. Katsaros James Kirkpatrick Jeremias Knoblauch M. de Kruijf Andreas V. Kuhlmann Shiro Kuriwaki Edward A. Laird Balaji Lakshminarayanan Rob Lamb Sophie Laurie Ho Chung Leon Law Amber Leeson Dominic T. Lennon David Leslie Haoxuan Li 0001 Zhu Li Jiawei Liu Wenqin Liu Samuel Livingstone Maria LomeliMaria Lomeli Garcia Jean-Michel Loubes Qinghua Lu 0001 Tim C. D. Lucas Diego Martinez-Taboada Marcos Matabuena Malcolm McMillan Xiao-Li Meng Jovana Mitrovic Arash Mohammadi 0001 H. Moon Hyungil Moon Peter Moskvichev Krikamol Muandet Andreas Müller 0008 V. Nguyen Vu Nguyen Anh Vu Nguyen-NgocAnh Vu Nguyen Geoff K. Nicholls Vatsala Nundloll Chris J. Oates Dino Oglic Rafael Oliveira 0001 Cheng Soon Ong S. B. Orbell Michael A. Osborne Oluwole K. Oyebamiji Oscar Hernan Madrid Padilla Brooks Paige Mijung Park Adrián Pérez-SuayAdrian Perez-Suay Jordan Phillipson Robert J. Piechocki Aleksandra Pizurica Vishakan Ponnampalam Massimiliano Pontil Vicky Pope Rachel Prudden Xingyue Pu Weizhi Quan Anant Raj Aaditya Ramdas Damith Chinthana RanasingheDamith C. Ranasinghe Markus Reichstein Stefan Reis David Rindt Tim G. J. Rudner Jakob Runge Maria Salama Faiza Samreen Antonin Schrab Jonas Schuff Ingmar Schuster Vojin Senk Brandon Severin Afshar Shamsi Eiki Shimizu William SimmWill Simm Vitaly Skachek Bharath K. Sriperumbudur Lina Stankovic Vladimir Stankovic 0001 David Steinsaltz Philip Stier Barnaby van Straaten Heiko Strathmann Roger Street Weijie J. Su Danica J. Sutherland Samuel Sutherland Simon Svab Zoltán Szabó 0001 Yee Whye Teh Damien Teney Lauren Thornton Jean-Francois TonJean-François Ton Ross Towe Russell Tsuchida Juan Carlos Vidal Massimo Vieno Florian Vigneau Dejan Vukobratovic Joanne A. Waller John Watkins Duncan Watson-Parris Veit WildVeit D. WildVeit David Wild Frank D. Wood James Wu Boming Xia Zhenchang Xing Zixiang Xiong Xiwei Xu 0001 Minhui Xue 0001 Christopher Yau Liuqi Yu Daokun Zhang Liang Zhang Mengyan Zhang 0001 Qinyi Zhang 0002 Yuqi Zhang Peilin Zhao Liming Zhu 0001 Dominik M. Zumbühl Aoqi Zuo