Tamara Broderick

Most-cited papers

Transparency and reproducibility in artificial intelligence
B. Haibe-Kains, George Alexandru Adam, A. Hosny, F. Khodakarami +27 more
2020Nature463 citations Open access
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Streaming Variational Bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson +1 more
2013Neural Information Processing Systems377 citations
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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Brian L. Trippe, Jason Yim, D. Tischer, D. Baker +3 more
2022International Conference on Learning Representations369 citations Open access
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Coresets for Scalable Bayesian Logistic Regression
Jonathan Huggins, Trevor Campbell, Tamara Broderick
2016Neural Information Processing Systems238 citations
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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Trevor Campbell, Tamara Broderick
2018International Conference on Machine Learning155 citations
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Covariances, Robustness, and Variational Bayes
Ryan Giordano, Tamara Broderick, Michael I. Jordan
2017Journal of machine learning research148 citations
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Automated Scalable Bayesian Inference via Hilbert Coresets
Trevor Campbell, Tamara Broderick
2017Journal of machine learning research139 citations
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A Swiss Army Infinitesimal Jackknife
Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan +1 more
2018International Conference on Artificial Intelligence and Statistics129 citations
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Ellipticity of dark matter haloes with galaxy–galaxy weak lensing
R. Mandelbaum, C. Hirata, Tamara Broderick, U. Seljak +1 more
2005107 citations Open access
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Beta Processes, Stick-Breaking and Power Laws
Tamara Broderick, Michael I. Jordan, J. Pitman
2011101 citations Open access
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MAD-Bayes: MAP-based Asymptotic Derivations from Bayes
Tamara Broderick, Brian Kulis, Michael I. Jordan
2012International Conference on Machine Learning97 citations
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Faster solutions of the inverse pairwise Ising problem
Tamara Broderick, Miroslav Dudík, G. Tkačik, R. Schapire +1 more
200794 citations
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Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick, Michael I. Jordan
2015Neural Information Processing Systems92 citations
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Boosting Variational Inference
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick +1 more
2016arXiv.org82 citations
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Feature allocations, probability functions, and paintboxes
Tamara Broderick, J. Pitman, Michael I. Jordan
201379 citations Open access
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Combinatorial Clustering and the Beta Negative Binomial Process
Tamara Broderick, L. Mackey, J. Paisley, Michael I. Jordan
2011IEEE Transactions on Pattern Analysis and Machine Intelligence73 citations Open access
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An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?
Tamara Broderick, Ryan Giordano, R. Meager
202052 citations
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Real-Time Semiparametric Regression
J. Luts, Tamara Broderick, M. Wand
201250 citations Open access
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Edge-exchangeable graphs and sparsity (NIPS 2016)
Diana Cai, Trevor Campbell, Tamara Broderick
201649 citations
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Validated Variational Inference via Practical Posterior Error Bounds
Jonathan Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019International Conference on Artificial Intelligence and Statistics46 citations
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Cluster and Feature Modeling from Combinatorial Stochastic Processes
Tamara Broderick, Michael I. Jordan, J. Pitman
201245 citations Open access
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Posteriors, conjugacy, and exponential families for completely random measures
Tamara Broderick, Ashia C. Wilson, Michael I. Jordan
2014Bernoulli42 citations Open access
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PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan Huggins, Ryan P. Adams, Tamara Broderick
2017Neural Information Processing Systems38 citations
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A Higher-Order Swiss Army Infinitesimal Jackknife
Ryan Giordano, Michael I. Jordan, Tamara Broderick
2019arXiv.org35 citations
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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2018arXiv.org35 citations
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Optimistic Concurrency Control for Distributed Unsupervised Learning
Xing-Hao Pan, Joseph E. Gonzalez, S. Jegelka, Tamara Broderick +1 more
2013Neural Information Processing Systems35 citations
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Approximate Cross-Validation in High Dimensions with Guarantees
William T. Stephenson, Tamara Broderick
2019International Conference on Artificial Intelligence and Statistics33 citations
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Redshift Accuracy Requirements for Future Supernova and Number Count Surveys
D. Huterer, A. Kim, L. Krauss, Tamara Broderick
200432 citations Open access
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More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics
L. Masoero, F. Camerlenghi, S. Favaro, Tamara Broderick
2019Biometrika31 citations Open access
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The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj Agrawal, Jonathan Huggins, Brian L. Trippe, Tamara Broderick
2019International Conference on Machine Learning30 citations
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Exchangeable Trait Allocations
Trevor Campbell, Diana Cai, Tamara Broderick
201629 citations Open access
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An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?
Tamara Broderick, Ryan Giordano, R. Meager
202028 citations
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Truncated random measures
Trevor Campbell, Jonathan Huggins, J. How, Tamara Broderick
2016Bernoulli27 citations Open access
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Fast and Flexible Selection with a Single Switch
Tamara Broderick, D. MacKay
2009PLoS ONE27 citations Open access
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Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
2020International Conference on Machine Learning26 citations
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Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Ryan Giordano, Martin Ingram, Tamara Broderick
2023Journal of machine learning research23 citations Open access
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Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick
2018Bayesian Analysis23 citations Open access
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Toward a taxonomy of trust for probabilistic machine learning
Tamara Broderick, A. Gelman, R. Meager, Anna L. Smith +1 more
2021Science Advances21 citations Open access
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Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Raj Agrawal, Caroline Uhler, Tamara Broderick
2018International Conference on Machine Learning21 citations
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Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick
2018International Conference on Artificial Intelligence and Statistics21 citations
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Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation
F. Camerlenghi, S. Favaro, L. Masoero, Tamara Broderick
2021Journal of the American Statistical Association20 citations Open access
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Approximate Cross-Validation for Structured Models
Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande +1 more
2020Neural Information Processing Systems19 citations
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Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano +4 more
2023International Conference on Machine Learning18 citations Open access
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Measuring the robustness of Gaussian processes to kernel choice
William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin +2 more
2021International Conference on Artificial Intelligence and Statistics17 citations
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Closed-Form Approximations of First-Passage Distributions for a Stochastic Decision-Making Model
Tamara Broderick, KongFatt Wong-Lin, P. Holmes
2009Applied Mathematics Research eXpress17 citations Open access
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Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Jonathan Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2018International Conference on Artificial Intelligence and Statistics16 citations
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Classification and Categorical Inputs with Treed Gaussian Process Models
Tamara Broderick, R. Gramacy
2009Journal of Classification16 citations Open access
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Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
2021Neural Information Processing Systems15 citations
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RAPID, MACHINE-LEARNED RESOURCE ALLOCATION: APPLICATION TO HIGH-REDSHIFT GAMMA-RAY BURST FOLLOW-UP
A. Morgan, J. Long, J. Richards, Tamara Broderick +2 more
201115 citations Open access
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Many processors, little time: MCMC for partitions via optimal transport couplings
Tin D. Nguyen, Brian L. Trippe, Tamara Broderick
2022International Conference on Artificial Intelligence and Statistics14 citations
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