Tamara Broderick

According to our database, Tamara Broderick authored at least 51 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Are you using test log-likelihood correctly?

Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick

Trans. Mach. Learn. Res., 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

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

Multi-marginal Schrödinger Bridges with Iterative Reference Refinement.

Yunyi Shen, Renato Berlinghieri, Tamara Broderick

CoRR, 2024

Consistent Validation for Predictive Methods in Spatial Settings.

David R. Burt, Yunyi Shen, Tamara Broderick

CoRR, 2024

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

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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Usability Study of Nomon: A Flexible Interface for Single-Switch Users.

Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick

Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility, 2023

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, 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

CoRR, 2022

Demonstrating Nomon: A Flexible Interface for Noisy Single-Switch Users.

Nicholas Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users.

Nicholas Ryan Bonaker, Emli-Mari Nel, Keith Vertanen, Tamara Broderick

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Many processors, little time: MCMC for partitions via optimal transport couplings.

Tin D. Nguyen, Brian L. Trippe, Tamara Broderick

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Toward a Taxonomy of Trust for Probabilistic Machine Learning.

Tamara Broderick, Andrew Gelman, Rachael Meager, Anna L. Smith, Tian Zheng

CoRR, 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

For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets.

Brian L. Trippe, Hilary K. Finucane, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.

William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Finite mixture models do not reliably learn the number of components.

Diana Cai, Trevor Campbell, Tamara Broderick

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Approximate Cross-Validation with Low-Rank Data in High Dimensions.

William T. Stephenson, Madeleine Udell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Approximate Cross-Validation for Structured Models.

Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Approximate Cross-Validation in High Dimensions with Guarantees.

William T. Stephenson, Tamara Broderick

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Validated Variational Inference via Practical Posterior Error Bounds.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Automated Scalable Bayesian Inference via Hilbert Coresets.

Trevor Campbell, Tamara Broderick

J. Mach. Learn. Res., 2019

Practical Posterior Error Bounds from Variational Objectives.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

CoRR, 2019

A Higher-Order Swiss Army Infinitesimal Jackknife.

Ryan Giordano, Michael I. Jordan, Tamara Broderick

CoRR, 2019

Sparse Approximate Cross-Validation for High-Dimensional GLMs.

William T. Stephenson, Tamara Broderick

CoRR, 2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations.

Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions.

Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

A Swiss Army Infinitesimal Jackknife.

Ryan Giordano, William T. Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Data-dependent compression of random features for large-scale kernel approximation.

Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Covariances, Robustness, and Variational Bayes.

Ryan Giordano, Tamara Broderick, Michael I. Jordan

J. Mach. Learn. Res., 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

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

CoRR, 2018

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent.

Trevor Campbell, Tamara Broderick

Proceedings of the 35th International Conference on Machine Learning, 2018

Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models.

Raj Agrawal, Caroline Uhler, Tamara Broderick

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference.

Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Boosting Variational Inference.

Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson

CoRR, 2016

Coresets for Scalable Bayesian Logistic Regression.

Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Edge-exchangeable graphs and sparsity.

Diana Cai, Trevor Campbell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

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

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes.

Ryan Giordano, Tamara Broderick, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Clusters and Features from Combinatorial Stochastic Processes.

Tamara Broderick

PhD thesis, 2014

Covariance Matrices for Mean Field Variational Bayes.

Ryan Giordano, Tamara Broderick

CoRR, 2014

2013

Optimistic Concurrency Control for Distributed Unsupervised Learning.

Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Streaming Variational Bayes.

Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes.

Tamara Broderick, Brian Kulis, Michael I. Jordan

Proceedings of the 30th International Conference on Machine Learning, 2013

2011

Classification and Categorical Inputs with Treed Gaussian Process Models.

Tamara Broderick, Robert B. Gramacy

J. Classif., 2011

2010

Combining Spatial and Telemetric Features for Learning Animal Movement Models.

Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick

Proceedings of the UAI 2010, 2010

2009

Fast and flexible selection with a single switch

Tamara Broderick, David John Cameron MacKay

CoRR, 2009