Edge-exchangeable graphs and sparsity. - Diana Cai; Trevor Campbell; Tamara Broderick (NIPS, 2016)

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

Weighted Meta-Learning. - Diana Cai; Rishit Sheth; Lester Mackey; Nicoló Fusi (CoRR, 2020)

Finite mixture models do not reliably learn the number of components. - Diana Cai; Trevor Campbell; Tamara Broderick (ICML, 2021)

Slice Sampling Reparameterization Gradients. - David M. Zoltowski; Diana Cai; Ryan P. Adams (NeurIPS, 2021)

Active multi-fidelity Bayesian online changepoint detection. - Gregory W. Gundersen; Diana Cai; Chuteng Zhou; Barbara E. Engelhardt; Ryan P. Adams (UAI, 2021)

Multi-fidelity Monte Carlo: a pseudo-marginal approach. - Diana Cai; Ryan P. Adams (NeurIPS, 2022)

Kernel Density Bayesian Inverse Reinforcement Learning. - Aishwarya Mandyam; Didong Li; Diana Cai; Andrew Jones; Barbara E. Engelhardt (CoRR, 2023)

Probabilistic Inference When the Model Is Wrong - Diana Cai (2023)

Batch and match: black-box variational inference with a score-based divergence. - Diana Cai; Chirag Modi 002; Loucas Pillaud-Vivien; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (ICML, 2024)

EigenVI: score-based variational inference with orthogonal function expansions. - Diana Cai; Chirag Modi 002; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (NeurIPS, 2024)

KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning. - Aishwarya Mandyam; Didong Li; Andrew Jones; Diana Cai; Barbara E. Engelhardt (Trans. Mach. Learn. Res., 2024)

Batch, match, and patch: low-rank approximations for score-based variational inference. - Chirag Modi 002; Diana Cai; Lawrence K. Saul (AISTATS, 2025)