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Interests
Machine Learning, Probabilistic Models, Optimization, Inference
Selected Recent Publications
U-Statistics for Importance-Weighted Variational Inference, TMLR 2023 (with J. Burroni, K. Takatsu, D. Sheldon)
Langevin Diffusion Variational Inference, AISTATS 2022 (with T. Geffner)
Variational Inference with Locally Enhanced Bounds for Hierarchical Models, ICML 2022 (with T. Geffner)
Variational Marginal Particle Filters, AISTATS 2022 (with J. Lai and D. Sheldon)
Amortized Variational Inference for Simple Hierarchical Models, NeurIPS 2021 (with A. Agrawal)
MCMC Variational Inference via Uncorrected Hamiltonian Annealing, NeurIPS 2021 (with T. Geffner)
On the Difficulty of Unbiased Alpha Divergence Minimization, ICML 2021 (with T. Geffner) [talk] [poster] [slides]
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization, NeurIPS 2020 (with A. Agrawal and D. Sheldon) [code]
Approximation Based Variance Reduction for Reparameterization Gradients, NeurIPS 2020 (with T. Geffner)
Provable Smoothness Guarantees for Black-Box Variational Inference, ICML 2020 [talk] [slides]
A Rule for Gradient Estimator Selection, with an Application to Variational Inference, AISTATS 2020 (with T. Geffner) [talk]
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation, NeurIPS 2019 (with D. Sheldon) [slides] [poster] [talk]
Provable Gradient Variance Guarantees for Black-Box Variational Inference, NeurIPS 2019 [poster]
Thompson Sampling and Approximate Inference, NeurIPS 2019 (with M. Phan and Y. Abbasi-Yadkori)
Using Large Ensembles of Control Variates for Variational Inference, NeurIPS 2018 (with T. Geffner)
Importance Weighting and Variational Inference, NeurIPS 2018 (with D. Sheldon) [poster]
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