| 2025 | UAI | Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. | Naitong Chen, Jonathan H. Huggins, Trevor Campbell |
| 2023 | AISTATS | A Targeted Accuracy Diagnostic for Variational Approximations. | Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins |
| 2020 | AISTATS | Validated Variational Inference via Practical Posterior Error Bounds. | Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick |
| 2019 | AISTATS | Data-dependent compression of random features for large-scale kernel approximation. | Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick |
| 2019 | AISTATS | Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. | Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick |
| 2019 | ICML | The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. | Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick |
| 2019 | ICML | LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. | Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick |
| 2015 | ICML | JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. | Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka |
| 2015 | ICML | Risk and Regret of Hierarchical Bayesian Learners. | Jonathan H. Huggins, Joshua B. Tenenbaum |
| 2014 | ISAIM | Toward a Theory of Pattern Discovery. | Jonathan H. Huggins, Cynthia Rudin |
| 2014 | SDM | A Statistical Learning Theory Framework for Supervised Pattern Discovery. | Jonathan H. Huggins, Cynthia Rudin |