Tamara Broderick

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

Achievements

+13 more ↓

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (14) 🌍 Conference Polyglot (5)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🌱 Topic Pioneer 🏆 Keyword Champion (2) 🔬 Deep Specialist (15) 🗃️ Keyword Collector (133) ⚡ Prolific Year (6) 🚀 Conference Pioneer 💎 Century Club (32) 🔥 Unstoppable (11) 📈 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 Schrödinger 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