Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications. - Rongrong Zhang; Wei Deng 002; Yu Michael Zhu (ACML, 2017)

An Adaptive Empirical Bayesian Method for Sparse Deep Learning. - Wei Deng 002; Xiao Zhang; Faming Liang; Guang Lin 001 (NeurIPS, 2019)

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. - Wei Deng 002; Qi Feng 005; Liyao Gao; Faming Liang; Guang Lin 001 (ICML, 2020)

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. - Wei Deng 002; Guang Lin 001; Faming Liang (NeurIPS, 2020)

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. - Wei Deng 002; Junwei Pan; Tian Zhou 006; Aaron Flores 001; Guang Lin 001 (CoRR, 2020)

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. - Wei Deng 002; Qi Feng 005; Georgios Karagiannis; Guang Lin 001; Faming Liang (ICLR, 2021)

Information Directed Sampling for Sparse Linear Bandits. - Botao Hao; Tor Lattimore; Wei Deng 002 (NeurIPS, 2021)

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. - Wei Deng 002; Junwei Pan; Tian Zhou 006; Deguang Kong; Aaron Flores 001; Guang Lin 001 (WSDM, 2021)

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications. - Yating Wang; Wei Deng 002; Guang Lin 001 (J. Comput. Phys., 2021)

An adaptive Hessian approximated stochastic gradient MCMC method. - Yating Wang; Wei Deng 002; Guang Lin 001 (J. Comput. Phys., 2021)

Interacting Contour Stochastic Gradient Langevin Dynamics. - Wei Deng 002; Siqi Liang 005; Botao Hao; Guang Lin 001; Faming Liang (ICLR, 2022)

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. - Wei Deng 002; Guang Lin 001; Faming Liang (Stat. Comput., 2022)

Non-reversible Parallel Tempering for Deep Posterior Approximation. - Wei Deng 002; Qian Zhang 067; Qi Feng 005; Faming Liang; Guang Lin 001 (AAAI, 2023)

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. - Yu Chen; Wei Deng 002; Shikai Fang; Fengpei Li; Nicole Tianjiao Yang; Yikai Zhang 003; Kashif Rasul; Shandian Zhe; Anderson Schneider; Yuriy Nevmyvaka (ICML, 2023)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. - Haoyang Zheng; Wei Deng 002; Christian Moya; Guang Lin 001 (AISTATS, 2024)

Variational Schrödinger Diffusion Models. - Wei Deng 002; Weijian Luo; Yixin Tan; Marin Bilos; Yu Chen; Yuriy Nevmyvaka; Ricky T. Q. Chen (ICML, 2024)

Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. - Haoyang Zheng; Hengrong Du; Qi Feng 005; Wei Deng 002; Guang Lin 001 (ICML, 2024)

Reflected Schrödinger Bridge for Constrained Generative Modeling. - Wei Deng 002; Yu Chen; Nicole Tianjiao Yang; Hengrong Du; Qi Feng 005; Ricky Tian Qi Chen (UAI, 2024)

On Convergence of Federated Averaging Langevin Dynamics. - Wei Deng 002; Qian Zhang 067; Yian Ma; Zhao Song 002; Guang Lin 001 (UAI, 2024)

Recurrent Interpolants for Probabilistic Time Series Prediction. - Yu Chen; Marin Bilos; Sarthak Mittal; Wei Deng 002; Kashif Rasul; Anderson Schneider (CoRR, 2024)

Optimal Stochastic Trace Estimation in Generative Modeling. - Xinyang Liu; Hengrong Du; Wei Deng 002; Ruqi Zhang (AISTATS, 2025)

Variational Schrödinger Momentum Diffusion. - Kevin Rojas; Yixin Tan; Molei Tao; Yuriy Nevmyvaka; Wei Deng 002 (AISTATS, 2025)

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. - Jiajun Liang; Qian Zhang 067; Wei Deng 002; Qifan Song; Guang Lin 001 (J. Comput. Graph. Stat., 2025)