| 2025 | AISTATS | Consistent Validation for Predictive Methods in Spatial Settings. | David R. Burt, Yunyi Shen, Tamara Broderick |
| 2025 | AISTATS | Multi-marginal Schrdinger Bridges with Iterative Reference Refinement. | Yunyi Shen, Renato Berlinghieri, Tamara Broderick |
| 2023 | ASSETS | A Usability Study of Nomon: A Flexible Interface for Single-Switch Users. | Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick |
| 2023 | ICLR | Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. | Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | ICML | Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. | Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. zgkmen, Junfei Xia, Tamara Broderick |
| 2022 | AISTATS | Many processors, little time: MCMC for partitions via optimal transport couplings. | Tin D. Nguyen, Brian L. Trippe, Tamara Broderick |
| 2022 | AISTATS | Measuring the robustness of Gaussian processes to kernel choice. | William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick |
| 2022 | CHI | A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. | Nicholas Ryan Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick |
| 2022 | CHI | Demonstrating Nomon: A Flexible Interface for Noisy Single-Switch Users. | Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick |
| 2021 | ICML | Finite mixture models do not reliably learn the number of components. | Diana Cai, Trevor Campbell, Tamara Broderick |
| 2020 | AISTATS | Validated Variational Inference via Practical Posterior Error Bounds. | Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick |
| 2020 | AISTATS | Approximate Cross-Validation in High Dimensions with Guarantees. | William T. Stephenson, 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 | A Swiss Army Infinitesimal Jackknife. | Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, 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 |
| 2018 | ICML | Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. | Raj Agrawal, Caroline Uhler, Tamara Broderick |
| 2018 | ICML | Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. | Trevor Campbell, Tamara Broderick |
| 2013 | ICML | MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. | Tamara Broderick, Brian Kulis, Michael I. Jordan |
| 2010 | UAI | Combining Spatial and Telemetric Features for Learning Animal Movement Models. | Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick |