Selected Publications

Diffusion Models

• X. Liu, H. Du, W. Deng, R. Zhang. Optimal Stochastic Trace Estimation in Generative Modeling. AISTATS 2025.

• K. Rojas, Y. Tan, M. Tao, Y. Nevmyvaka, W. Deng. Variational Schrödinger Momentum Diffusion. AISTATS 2025.

• W. Deng, W. Luo, Y. Tan, M. Biloš, Yu, Yuriy, T. Q. Chen. Variational Schrödinger Diffusion Models. ICML 2024.

• W. Deng, Y. Chen, T. Yang, H. Du, Q. Feng, T. Q. Chen. Reflected Schrödinger Bridge for Constrained Generative Modeling. UAI 2024 (Oral) Acceptance rate 3.8%.

• Y. Chen, W. Deng, S. Fang, F. Li, T. Yang, Y. Zhang, K. Rasul, S. Zhe, A. Schneider, Y. Nevmyvaka. Provably Convergent Schrödinger Bridge with Appli. to Prob. Time Series Imputation. ICML 2023.

Thompson Sampling

• H Zheng, W Deng, C Moya, G Lin. Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. AISTAT 2024

• B. Hao, T. Lattimore, W. Deng. Information Directed Sampling for Sparse Linear Bandits. NeurIPS 2021 Spotlight (3% acceptance rate)

Monte Carlo Methods

• J. Liang, Q. Zhang, W. Deng, Q. Song, G. Lin. Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. Journal of Computational and Graphical Statistics. 2024

• W. Deng, G. Lin, F. Liang. An Adaptively Weighted Stochastic Gradient MCMC Algorithm for Monte Carlo simulation and Global Optimization. Statistics and Computing, (2022) 32:58 [code]

• W. Deng, S. Liang, B. Hao, G. Lin, F. Liang. Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR 2022 [code] [video]

• W. Deng, Q. Feng, L. Gao, F. Liang, G. Lin. Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. ICML 2020. [code] [slides]

• W. Deng, G. Lin, F. Liang. A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. NeurIPS 2020 [code] [blog] [slides] [poster][video] [知乎]