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ORCIDJonathan H. Huggins
- > Home > Persons > Jonathan H. Huggins
Publications
- 2026
[i24]Yu Wang, Jie Ding, Jonathan H. Huggins:
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo. CoRR abs/2606.00293 (2026)- [i23]Xiaoyu Wang, Jonathan H. Huggins:
Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo. CoRR abs/2606.00309 (2026) - [i22]Trevor Campbell, Jonathan H. Huggins, Kyurae Kim, Charles C. Margossian:
Large-scale empirical tuning and comparison of default optimizers for variational inference. CoRR abs/2606.07841 (2026) - 2025
- [c16]Naitong Chen, Jonathan H. Huggins, Trevor Campbell:
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. UAI 2025: 647-672 - [i21]Xiaoyu Wang, Mikolaj J. Kasprzak, Jeffrey Negrea, Solesne Bourguin, Jonathan H. Huggins:
Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms. CoRR abs/2501.12212 (2025) - 2024
- [j1]Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. 25: 219:1-219:71 (2024) - [i20]Naitong Chen, Jonathan H. Huggins, Trevor Campbell:
Tuning-free coreset Markov chain Monte Carlo. CoRR abs/2410.18973 (2024) - 2023
- [c15]Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins:
A Targeted Accuracy Diagnostic for Variational Approximations. AISTATS 2023: 8351-8372 - [i19]Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins:
A Targeted Accuracy Diagnostic for Variational Approximations. CoRR abs/2302.12419 (2023) - 2022
- [i17]Manushi Welandawe
, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
Robust, Automated, and Accurate Black-box Variational Inference. CoRR abs/2203.15945 (2022) - [i16]Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins:
Statistical Inference with Stochastic Gradient Algorithms. CoRR abs/2207.12395 (2022) - 2021
- [c14]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS 2021: 7787-7798 - [i15]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High-dimensional Variational Inference. CoRR abs/2103.01085 (2021) - 2020
- [c13]Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick:
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS 2020: 1792-1802 - [c12]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS 2020 - [i14]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR abs/2009.00666 (2020) - 2019
- [c11]Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS 2019: 796-805 - [c10]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. AISTATS 2019: 1822-1831 - [c9]Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML 2019: 141-150 - [c8]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML 2019: 6315-6324 - [i13]Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR abs/1905.06501 (2019) - [i12]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR abs/1905.07499 (2019) - [i11]Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick:
Practical Posterior Error Bounds from Variational Objectives. CoRR abs/1910.04102 (2019) - 2018
- [c7]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. NeurIPS 2018: 1903-1913 - [i10]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. CoRR abs/1806.07788 (2018) - [i9]Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR abs/1806.10234 (2018) - [i8]Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick:
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR abs/1809.09505 (2018) - [i7]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. CoRR abs/1810.04249 (2018) - [i6]Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick:
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR abs/1811.11790 (2018) - 2017
- [c6]Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick:
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS 2017: 3611-3621 - 2016
- [c5]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. NIPS 2016: 4080-4088 - [i5]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. CoRR abs/1605.06423 (2016) - 2015
- [c4]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML 2015: 693-701 - [c3]Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners. ICML 2015: 1442-1451 - [i4]Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson:
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR abs/1501.00052 (2015) - [i3]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR abs/1503.00332 (2015) - [i2]Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners. CoRR abs/1505.04984 (2015)
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