← Authors

Tamara Broderick

32 papers · 2013–2025 · 5 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (12) 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (12) πŸ—ΊοΈ Taxonomy Completionist (14) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (15) πŸ—ƒοΈ Keyword Collector (133) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (32) πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter ❓ The Questioner (2)

Conferences

NIPS (10) AISTATS (9) ICML (7) JMLR (5) ICLR (1)

Top co-authors

Trevor Campbell (8) Jonathan Huggins (6) Raj Agrawal (5) Ryan Giordano (4) Michael I. Jordan (4) Brian L. Trippe (3) Michael I Jordan (3) Brian Trippe (3) Will Stephenson (3) Mikolaj Kasprzak (2)

Research topics

Statistics (1)

Keywords

bayesian inference (9) markov chain monte carlo (7) posterior approximation (6) variational inference (4) posterior inference (4) uncertainty quantification (4) gaussian process (4) laplace approximation (3) posterior mean (2) logistic regression (2) error bound (2) generalized linear model (2) model selection (2) variational baye (2) kernel methods (2) bayesian posterior (2) feature learning (2) low-rank approximation (2) coreset construction (2) bayesian coreset (2)

Papers

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences JMLR 2025
Multi-marginal SchrΓΆdinger Bridges with Iterative Reference Refinement AISTATS 2025
Consistent Validation for Predictive Methods in Spatial Settings AISTATS 2025
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box JMLR 2024
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time JMLR 2023
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents ICML 2023
Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem ICLR 2023
Many processors, little time: MCMC for partitions via optimal transport couplings AISTATS 2022
Measuring the robustness of Gaussian processes to kernel choice AISTATS 2022
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression NIPS 2021
Finite mixture models do not reliably learn the number of components ICML 2021
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets NIPS 2021
Approximate Cross-Validation with Low-Rank Data in High Dimensions NIPS 2020
Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020
Approximate Cross-Validation in High Dimensions with Guarantees AISTATS 2020
Approximate Cross-Validation for Structured Models NIPS 2020
Data-dependent compression of random features for large-scale kernel approximation AISTATS 2019
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions ICML 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations ICML 2019
Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees AISTATS 2019
A Swiss Army Infinitesimal Jackknife AISTATS 2019
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent ICML 2018
Covariances, Robustness, and Variational Bayes JMLR 2018
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference NIPS 2017
Coresets for Scalable Bayesian Logistic Regression NIPS 2016
Edge-exchangeable graphs and sparsity NIPS 2016
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NIPS 2015
Optimistic Concurrency Control for Distributed Unsupervised Learning NIPS 2013
Streaming Variational Bayes NIPS 2013
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes ICML 2013