Tamara Broderick

83 papers A* 12A 11Journal 51Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Do LLMs Benefit From Their Own Words? CoRR Jenny Y. Huang, Leshem Choshen, Ramón Fernandez Astudillo, Tamara Broderick, Jacob Andreas
2026	J	`jnl`	Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition. CoRR Manuel Quintero, Advik Shreekumar, William T. Stephenson, Tamara Broderick
2025	J	`jnl`	Approximations to worst-case data dropping: unmasking failure modes. Trans. Mach. Learn. Res. Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick
2025	J	`jnl`	Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations. CoRR Manuel Quintero, William T. Stephenson, Advik Shreekumar, Tamara Broderick
2025	A	`conf`	Consistent Validation for Predictive Methods in Spatial Settings. AISTATS David R. Burt, Yunyi Shen, Tamara Broderick
2025	J	`jnl`	Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings. CoRR Jenny Y. Huang, Yunyi Shen, Dennis Wei, Tamara Broderick
2025	J	`jnl`	How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences. J. Mach. Learn. Res. Mikolaj J. Kasprzak, Ryan Giordano, Tamara Broderick
2025	J	`jnl`	Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for Spatial Linear Models. CoRR David R. Burt, Renato Berlinghieri, Stephen Bates, Tamara Broderick
2025	A	`conf`	Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. AISTATS Yunyi Shen, Renato Berlinghieri, Tamara Broderick
2025	J	`jnl`	Network and Risk Analysis of Surety Bonds. CoRR Tamara Broderick, Ali Jadbabaie, Vanessa Lin, Manuel Quintero, Arnab Sarker, Sean R. Sinclair
2025	J	`jnl`	Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schrödinger Bridge's End. CoRR Renato Berlinghieri, Yunyi Shen, Jialong Jiang, Tamara Broderick
2025	J	`jnl`	Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification. CoRR David R. Burt, Renato Berlinghieri, Tamara Broderick
2024	J	`jnl`	A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making. CoRR Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick
2024	J	`jnl`	Are you using test log-likelihood correctly? Trans. Mach. Learn. Res. Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick
2024	J	`jnl`	Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. J. Mach. Learn. Res. Ryan Giordano, Martin Ingram, Tamara Broderick
2024	J	`jnl`	Consistent Validation for Predictive Methods in Spatial Settings. CoRR David R. Burt, Yunyi Shen, Tamara Broderick
2024	J	`jnl`	Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. CoRR Yunyi Shen, Renato Berlinghieri, Tamara Broderick
2023	A	`conf`	A Usability Study of Nomon: A Flexible Interface for Single-Switch Users. ASSETS Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick
2023	J	`jnl`	Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. CoRR Ryan Giordano, Martin Ingram, Tamara Broderick
2023	A*	`conf`	Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. ICLR Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola
2023	A*	`conf`	Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. ICML Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
2023	J	`jnl`	Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. CoRR Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
2023	J	`jnl`	The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. J. Mach. Learn. Res. Raj Agrawal, Tamara Broderick
2022	A*	`conf`	A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. CHI Nicholas Ryan Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick
2022	J	`jnl`	A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. CoRR Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick
2022	J	`jnl`	Are you using test log-likelihood correctly? CoRR Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick
2022	—	`conf`	Demonstrating Nomon: A Flexible Interface for Noisy Single-Switch Users. CHI Extended Abstracts Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick
2022	J	`jnl`	Developing a Series of AI Challenges for the United States Department of the Air Force. CoRR Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James R. Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean E. Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L. Weiss, Allan B. Wollaber, Sophia Yuditskaya, Jeremy Kepner
2022	J	`jnl`	Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem. CoRR Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, Tommi S. Jaakkola
2022	A	`conf`	Many processors, little time: MCMC for partitions via optimal transport couplings. AISTATS Tin D. Nguyen, Brian L. Trippe, Tamara Broderick
2022	A	`conf`	Measuring the robustness of Gaussian processes to kernel choice. AISTATS William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
2021	A*	`conf`	Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. NeurIPS William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
2021	J	`jnl`	Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. CoRR William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
2021	A*	`conf`	Finite mixture models do not reliably learn the number of components. ICML Diana Cai, Trevor Campbell, Tamara Broderick
2021	A*	`conf`	For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. NeurIPS Brian L. Trippe, Hilary K. Finucane, Tamara Broderick
2021	J	`jnl`	For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. CoRR Brian L. Trippe, Hilary K. Finucane, Tamara Broderick
2021	J	`jnl`	Measuring the sensitivity of Gaussian processes to kernel choice. CoRR William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
2021	J	`jnl`	Toward a Taxonomy of Trust for Probabilistic Machine Learning. CoRR Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng
2020	A*	`conf`	Approximate Cross-Validation for Structured Models. NeurIPS Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick
2020	J	`jnl`	Approximate Cross-Validation for Structured Models. CoRR Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick
2020	A	`conf`	Approximate Cross-Validation in High Dimensions with Guarantees. AISTATS William T. Stephenson, Tamara Broderick
2020	A*	`conf`	Approximate Cross-Validation with Low-Rank Data in High Dimensions. NeurIPS William T. Stephenson, Madeleine Udell, Tamara Broderick
2020	J	`jnl`	Approximate Cross-Validation with Low-Rank Data in High Dimensions. CoRR William T. Stephenson, Madeleine Udell, Tamara Broderick
2020	A	`conf`	Validated Variational Inference via Practical Posterior Error Bounds. AISTATS Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	J	`jnl`	A Higher-Order Swiss Army Infinitesimal Jackknife. CoRR Ryan Giordano, Michael I. Jordan, Tamara Broderick
2019	A	`conf`	A Swiss Army Infinitesimal Jackknife. AISTATS Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick
2019	J	`jnl`	Automated Scalable Bayesian Inference via Hilbert Coresets. J. Mach. Learn. Res. Trevor Campbell, Tamara Broderick
2019	A	`conf`	Data-dependent compression of random features for large-scale kernel approximation. AISTATS Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2019	A*	`conf`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	J	`jnl`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	J	`jnl`	Practical Posterior Error Bounds from Variational Objectives. CoRR Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	A	`conf`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2019	J	`jnl`	Sparse Approximate Cross-Validation for High-Dimensional GLMs. CoRR William T. Stephenson, Tamara Broderick
2019	A*	`conf`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick
2019	J	`jnl`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick
2018	A*	`conf`	Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. ICML Trevor Campbell, Tamara Broderick
2018	J	`jnl`	Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. CoRR Trevor Campbell, Tamara Broderick
2018	J	`jnl`	Covariances, Robustness, and Variational Bayes. J. Mach. Learn. Res. Ryan Giordano, Tamara Broderick, Michael I. Jordan
2018	J	`jnl`	Data-dependent compression of random features for large-scale kernel approximation. CoRR Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2018	A*	`conf`	Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. ICML Raj Agrawal, Caroline Uhler, Tamara Broderick
2018	J	`jnl`	Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models. CoRR Raj Agrawal, Tamara Broderick, Caroline Uhler
2018	J	`jnl`	Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2018	J	`jnl`	Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
2018	J	`jnl`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2017	J	`jnl`	Automated Scalable Bayesian Inference via Hilbert Coresets. CoRR Trevor Campbell, Tamara Broderick
2017	—	`conf`	PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
2016	J	`jnl`	Boosting Variational Inference. CoRR Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson
2016	—	`conf`	Coresets for Scalable Bayesian Logistic Regression. NIPS Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2016	J	`jnl`	Coresets for Scalable Bayesian Logistic Regression. CoRR Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2016	—	`conf`	Edge-exchangeable graphs and sparsity. NIPS Diana Cai, Trevor Campbell, Tamara Broderick
2015	J	`jnl`	Combinatorial Clustering and the Beta Negative Binomial Process. IEEE Trans. Pattern Anal. Mach. Intell. Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan
2015	—	`conf`	Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. NIPS Ryan Giordano, Tamara Broderick, Michael I. Jordan
2014	—	`—`	Clusters and Features from Combinatorial Stochastic Processes. Tamara Broderick
2014	J	`jnl`	Covariance Matrices for Mean Field Variational Bayes. CoRR Ryan Giordano, Tamara Broderick
2013	—	`conf`	MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. ICML (3) Tamara Broderick, Brian Kulis, Michael I. Jordan
2013	—	`conf`	Optimistic Concurrency Control for Distributed Unsupervised Learning. NIPS Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
2013	J	`jnl`	Optimistic Concurrency Control for Distributed Unsupervised Learning. CoRR Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
2013	—	`conf`	Streaming Variational Bayes. NIPS Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
2013	J	`jnl`	Streaming Variational Bayes. CoRR Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
2012	J	`jnl`	Combining Spatial and Telemetric Features for Learning Animal Movement Models CoRR Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
2011	J	`jnl`	Classification and Categorical Inputs with Treed Gaussian Process Models. J. Classif. Tamara Broderick, Robert B. Gramacy
2010	A	`conf`	Combining Spatial and Telemetric Features for Learning Animal Movement Models. UAI Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick
2009	J	`jnl`	Fast and flexible selection with a single switch CoRR Tamara Broderick, David John Cameron MacKay