Broderick, Tamara

48 publications

TMLR 2025 Approximations to Worst-Case Data Dropping: Unmasking Failure Modes Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick

ICLRW 2025 Approximations to Worst-Case Data Dropping: Unmasking Failure Modes Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick

ICLRW 2025 Beyond Schrödinger Bridges: A Least-Squares Approach for Learning Stochastic Dynamics with Unknown Volatility Renato Berlinghieri, Yunyi Shen, Tamara Broderick

ICLRW 2025 Common Functional Decompositions Can Mis-Attribute Differences in Outcomes Between Populations Manuel Quintero, William T. Stephenson, Advik Shreekumar, Tamara Broderick

AISTATS 2025 Consistent Validation for Predictive Methods in Spatial Settings David R. Burt, Yunyi Shen, Tamara Broderick

JMLR 2025 How Good Is Your Laplace Approximation of the Bayesian Posterior? Finite-Sample Computable Error Bounds for a Variety of Useful Divergences Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick

AISTATS 2025 Multi-Marginal Schrödinger Bridges with Iterative Reference Refinement Yunyi Shen, Renato Berlinghieri, Tamara Broderick

NeurIPS 2025 Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations David R. Burt, Renato Berlinghieri, Stephen Bates, Tamara Broderick

ICLRW 2025 Wild Posteriors in the Wild Yunyi Shen, Tamara Broderick

TMLR 2024 Are You Using Test Log-Likelihood Correctly? Sameer Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick

JMLR 2024 Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box Ryan Giordano, Martin Ingram, Tamara Broderick

ICMLW 2024 Consistent Validation for Predictive Methods in Spatial Settings David R. Burt, Yunyi Shen, Tamara Broderick

ICLRW 2024 Learning a Vector Field from Snapshots of Unidentified Particles Rather than Particle Trajectories Yunyi Shen, Renato Berlinghieri, Tamara Broderick

ICMLW 2024 Using Gradients to Check Sensitivity of MCMC-Based Analyses to Removing Data Tin D. Nguyen, Ryan James Giordano, Rachael Meager, Tamara Broderick

ICLR 2023 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

ICML 2023 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 Özgökmen, Junfei Xia, Tamara Broderick

ICLRW 2023 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 Özgökmen, Junfei Zia, Tamara Broderick

JMLR 2023 The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time Raj Agrawal, Tamara Broderick

AISTATS 2022 Many Processors, Little Time: MCMC for Partitions via Optimal Transport Couplings Tin D. Nguyen, Brian L. Trippe, Tamara Broderick

AISTATS 2022 Measuring the Robustness of Gaussian Processes to Kernel Choice William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer Deshpande, Tamara Broderick

NeurIPSW 2022 Are You Using Test Log-Likelihood Correctly? Sameer Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick

NeurIPSW 2022 Double Trouble: Predicting New Variant Counts Across Two Heterogeneous Populations Yunyi Shen, Lorenzo Masoero, Joshua Schraiber, Tamara Broderick

NeurIPS 2021 Can We Globally Optimize Cross-Validation Loss? Quasiconvexity in Ridge Regression Will Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

ICML 2021 Finite Mixture Models Do Not Reliably Learn the Number of Components Diana Cai, Trevor Campbell, Tamara Broderick

NeurIPS 2021 For High-Dimensional Hierarchical Models, Consider Exchangeability of Effects Across Covariates Instead of Across Datasets Brian Trippe, Hilary Finucane, Tamara Broderick

NeurIPS 2020 Approximate Cross-Validation for Structured Models Soumya Ghosh, Will Stephenson, Tin D Nguyen, Sameer Deshpande, Tamara Broderick

AISTATS 2020 Approximate Cross-Validation in High Dimensions with Guarantees William Stephenson, Tamara Broderick

NeurIPS 2020 Approximate Cross-Validation with Low-Rank Data in High Dimensions Will Stephenson, Madeleine Udell, Tamara Broderick

NeurIPSW 2020 Independent Versus Truncated Finite Approximations for Bayesian Nonparametric Inference Tin D. Nguyen, Jonathan H. Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick

NeurIPSW 2020 Power Posteriors Do Not Reliably Learn the Number of Components in a Finite Mixture Diana Cai, Trevor Campbell, Tamara Broderick

AISTATS 2020 Validated Variational Inference via Practical Posterior Error Bounds Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

AISTATS 2019 A Swiss Army Infinitesimal Jackknife Ryan Giordano, William Stephenson, Runjing Liu, Michael Jordan, Tamara Broderick

JMLR 2019 Automated Scalable Bayesian Inference via Hilbert Coresets Trevor Campbell, Tamara Broderick

AISTATS 2019 Data-Dependent Compression of Random Features for Large-Scale Kernel Approximation Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick

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

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

ICML 2019 The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick

ICML 2018 Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent Trevor Campbell, Tamara Broderick

JMLR 2018 Covariances, Robustness, and Variational Bayes Ryan Giordano, Tamara Broderick, Michael I. Jordan

ICML 2018 Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models Raj Agrawal, Caroline Uhler, Tamara Broderick

NeurIPS 2017 PASS-GLM: Polynomial Approximate Sufficient Statistics for Scalable Bayesian GLM Inference Jonathan Huggins, Ryan P. Adams, Tamara Broderick

NeurIPS 2016 Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick

NeurIPS 2016 Edge-Exchangeable Graphs and Sparsity Diana Cai, Trevor Campbell, Tamara Broderick

NeurIPS 2015 Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J Giordano, Tamara Broderick, Michael I Jordan

ICML 2013 MAD-Bayes: MAP-Based Asymptotic Derivations from Bayes Tamara Broderick, Brian Kulis, Michael Jordan

NeurIPS 2013 Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I Jordan

NeurIPS 2013 Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, Michael I Jordan

UAI 2010 Combining Spatial and Telemetric Features for Learning Animal Movement Models Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick