Cai, Diana

13 publications

AISTATS 2025 Batch, Match, and Patch: Low-Rank Approximations for Score-Based Variational Inference Chirag Modi, Diana Cai, Lawrence K. Saul

NeurIPS 2025 Fisher Meets Feynman: Score-Based Variational Inference with a Product of Experts Diana Cai, Robert M. Gower, David Blei, Lawrence K. Saul

ICML 2024 Batch and Match: Black-Box Variational Inference with a Score-Based Divergence Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul

NeurIPS 2024 EigenVI: Score-Based Variational Inference with Orthogonal Function Expansions Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul

ICMLW 2024 EigenVI: Score-Based Variational Inference with Orthogonal Function Expansions Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul

TMLR 2024 KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam, Didong Li, Andrew Jones, Diana Cai, Barbara E Engelhardt

NeurIPS 2022 Multi-Fidelity Monte Carlo: A Pseudo-Marginal Approach Diana Cai, Ryan P. Adams

UAI 2021 Active Multi-Fidelity Bayesian Online Changepoint Detection Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

ICML 2021 Finite Mixture Models Do Not Reliably Learn the Number of Components Diana Cai, Trevor Campbell, Tamara Broderick

NeurIPS 2021 Slice Sampling Reparameterization Gradients David Zoltowski, Diana Cai, Ryan P. Adams

NeurIPSW 2020 Power Posteriors Do Not Reliably Learn the Number of Components in a Finite Mixture Diana Cai, Trevor Campbell, Tamara Broderick

NeurIPS 2018 A Bayesian Nonparametric View on Count-Min Sketch Diana Cai, Michael Mitzenmacher, Ryan P. Adams

NeurIPS 2016 Edge-Exchangeable Graphs and Sparsity Diana Cai, Trevor Campbell, Tamara Broderick