Fast and flexible selection with a single switch - Tamara Broderick; David John Cameron MacKay (CoRR, 2009)

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

Classification and Categorical Inputs with Treed Gaussian Process Models. - Tamara Broderick; Robert B. Gramacy (J. Classif., 2011)

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes. - Tamara Broderick; Brian Kulis; Michael I. Jordan (ICML (3), 2013)

Streaming Variational Bayes. - Tamara Broderick; Nicholas Boyd; Andre Wibisono; Ashia C. Wilson; Michael I. Jordan (NIPS, 2013)

Optimistic Concurrency Control for Distributed Unsupervised Learning. - Xinghao Pan; Joseph E. Gonzalez; Stefanie Jegelka; Tamara Broderick; Michael I. Jordan (NIPS, 2013)

Covariance Matrices for Mean Field Variational Bayes. - Ryan Giordano; Tamara Broderick (CoRR, 2014)

Clusters and Features from Combinatorial Stochastic Processes. - Tamara Broderick (2014)

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (NIPS, 2015)

Combinatorial Clustering and the Beta Negative Binomial Process. - Tamara Broderick; Lester W. Mackey; John W. Paisley; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Edge-exchangeable graphs and sparsity. - Diana Cai; Trevor Campbell; Tamara Broderick (NIPS, 2016)

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

Boosting Variational Inference. - Fangjian Guo; Xiangyu Wang 006; Kai Fan 002; Tamara Broderick; David B. Dunson (CoRR, 2016)

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

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

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. - Trevor Campbell; Tamara Broderick (ICML, 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)

Covariances, Robustness, and Variational Bayes. - Ryan Giordano; Tamara Broderick; Michael I. Jordan (J. Mach. Learn. Res., 2018)

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

A Swiss Army Infinitesimal Jackknife. - Ryan Giordano; William T. Stephenson; Runjing Liu; Michael I. Jordan; 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)

Sparse Approximate Cross-Validation for High-Dimensional GLMs. - William T. Stephenson; Tamara Broderick (CoRR, 2019)

A Higher-Order Swiss Army Infinitesimal Jackknife. - Ryan Giordano; Michael I. Jordan; Tamara Broderick (CoRR, 2019)

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

Automated Scalable Bayesian Inference via Hilbert Coresets. - Trevor Campbell; Tamara Broderick (J. Mach. Learn. Res., 2019)

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

Approximate Cross-Validation in High Dimensions with Guarantees. - William T. Stephenson; Tamara Broderick (AISTATS, 2020)

Approximate Cross-Validation for Structured Models. - Soumya Ghosh; William T. Stephenson; Tin D. Nguyen; Sameer K. Deshpande; Tamara Broderick (NeurIPS, 2020)

Approximate Cross-Validation with Low-Rank Data in High Dimensions. - William T. Stephenson; Madeleine Udell; Tamara Broderick (NeurIPS, 2020)

Finite mixture models do not reliably learn the number of components. - Diana Cai; Trevor Campbell; Tamara Broderick (ICML, 2021)

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression. - William T. Stephenson; Zachary Frangella; Madeleine Udell; Tamara Broderick (NeurIPS, 2021)

For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets. - Brian L. Trippe; Hilary K. Finucane; Tamara Broderick (NeurIPS, 2021)

Measuring the sensitivity of Gaussian processes to kernel choice. - William T. Stephenson; Soumya Ghosh; Tin D. Nguyen; Mikhail Yurochkin; Sameer K. Deshpande; Tamara Broderick (CoRR, 2021)

Toward a Taxonomy of Trust for Probabilistic Machine Learning. - Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng (CoRR, 2021)

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 K. Deshpande; Tamara Broderick (AISTATS, 2022)

A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users. - Nicholas Ryan Bonaker; Emli-Mari Nel; Keith Vertanen; Tamara Broderick (CHI, 2022)

Demonstrating Nomon: A Flexible Interface for Noisy Single-Switch Users. - Nicholas Bonaker; Emli-Mari Nel; Keith Vertanen; Tamara Broderick (CHI Extended Abstracts, 2022)

Developing a Series of AI Challenges for the United States Department of the Air Force. - 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; et al. (CoRR, 2022)

A Usability Study of Nomon: A Flexible Interface for Single-Switch Users. - Nicholas Bonaker; Emli-Mari Nel; Keith Vertanen; Tamara Broderick (ASSETS, 2023)

Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem. - Brian L. Trippe; Jason Yim; Doug Tischer; David Baker 001; Tamara Broderick; Regina Barzilay; Tommi S. Jaakkola (ICLR, 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 M. Özgökmen; Junfei Xia; Tamara Broderick (ICML, 2023)

The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time. - Raj Agrawal; Tamara Broderick (J. Mach. Learn. Res., 2023)

A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making. - Renato Berlinghieri; David R. Burt; Paolo Giani; Arlene M. Fiore; Tamara Broderick (CoRR, 2024)

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. - Ryan Giordano; Martin Ingram; Tamara Broderick (J. Mach. Learn. Res., 2024)

Are you using test log-likelihood correctly? - Sameer K. Deshpande; Soumya Ghosh; Tin D. Nguyen; Tamara Broderick (Trans. Mach. Learn. Res., 2024)

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

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement. - Yunyi Shen; Renato Berlinghieri; Tamara Broderick (AISTATS, 2025)

Approximations to worst-case data dropping: unmasking failure modes. - Jenny Y. Huang; David R. Burt; Yunyi Shen; Tin D. Nguyen; Tamara Broderick (Trans. Mach. Learn. Res., 2025)

Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for Spatial Linear Models. - David R. Burt; Renato Berlinghieri; Stephen Bates; Tamara Broderick (CoRR, 2025)

Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations. - Manuel Quintero; William T. Stephenson; Advik Shreekumar; Tamara Broderick (CoRR, 2025)

Oh SnapMMD! Forecasting Stochastic Dynamics Beyond the Schrödinger Bridge's End. - Renato Berlinghieri; Yunyi Shen; Jialong Jiang; Tamara Broderick (CoRR, 2025)