Toward a Theory of Pattern Discovery. - Jonathan H. Huggins; Cynthia Rudin (ISAIM, 2014)

A Statistical Learning Theory Framework for Supervised Pattern Discovery. - Jonathan H. Huggins; Cynthia Rudin (SDM, 2014)

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. - Jonathan H. Huggins; Karthik Narasimhan; Ardavan Saeedi; Vikash Mansinghka 001 (ICML, 2015)

Risk and Regret of Hierarchical Bayesian Learners. - Jonathan H. Huggins; Joshua B. Tenenbaum (ICML, 2015)

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. - Jonathan H. Huggins; Ardavan Saeedi; Matthew J. Johnson 002 (CoRR, 2015)

Coresets for Scalable Bayesian Logistic Regression. - Jonathan H. Huggins; Trevor Campbell; Tamara Broderick (NIPS, 2016)

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. - Jonathan H. Huggins; Ryan P. Adams; Tamara Broderick (NIPS, 2017)

Random Feature Stein Discrepancies. - Jonathan H. Huggins; Lester Mackey (NeurIPS, 2018)

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. - Jonathan H. Huggins; Trevor Campbell; Mikolaj Kasprzak; Tamara Broderick (CoRR, 2018)

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. - Miriam Shiffman; William T. Stephenson; Geoffrey Schiebinger; Jonathan H. Huggins; Trevor Campbell; Aviv Regev; Tamara Broderick (CoRR, 2018)

Data-dependent compression of random features for large-scale kernel approximation. - Raj Agrawal; Trevor Campbell; Jonathan H. Huggins; Tamara Broderick (AISTATS, 2019)

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. - Jonathan H. Huggins; Trevor Campbell; Mikolaj Kasprzak; Tamara Broderick (AISTATS, 2019)

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. - Raj Agrawal; Brian L. Trippe; Jonathan H. Huggins; Tamara Broderick (ICML, 2019)

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. - Brian L. Trippe; Jonathan H. Huggins; Raj Agrawal; Tamara Broderick (ICML, 2019)

Practical Posterior Error Bounds from Variational Objectives. - Jonathan H. Huggins; Mikolaj Kasprzak; Trevor Campbell; Tamara Broderick (CoRR, 2019)

Validated Variational Inference via Practical Posterior Error Bounds. - Jonathan H. Huggins; Mikolaj Kasprzak; Trevor Campbell; Tamara Broderick (AISTATS, 2020)

Robust, Accurate Stochastic Optimization for Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Michael Riis Andersen; Måns Magnusson; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2020)

Challenges and Opportunities in High Dimensional Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Manushi Welandawe; Michael Riis Andersen; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2021)

Robust, Automated, and Accurate Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (CoRR, 2022)

Statistical Inference with Stochastic Gradient Algorithms. - Jeffrey Negrea; Jun Yang; Haoyue Feng; Daniel M. Roy 001; Jonathan H. Huggins (CoRR, 2022)

A Targeted Accuracy Diagnostic for Variational Approximations. - Yu Wang; Mikolaj Kasprzak; Jonathan H. Huggins (AISTATS, 2023)

Reproducible Parameter Inference Using Bagged Posteriors. - Jonathan H. Huggins; Jeffrey W. Miller (CoRR, 2023)

Tuning-free coreset Markov chain Monte Carlo. - Naitong Chen; Jonathan H. Huggins; Trevor Campbell (CoRR, 2024)

A Framework for Improving the Reliability of Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (J. Mach. Learn. Res., 2024)

Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. - Naitong Chen; Jonathan H. Huggins; Trevor Campbell (UAI, 2025)

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms. - Xiaoyu Wang; Mikolaj Kasprzak; Jeffrey Negrea; Solesne Bourguin; Jonathan H. Huggins (CoRR, 2025)