Selected Publications

K. Rojas, J. Lin, K. Rasul, A. Schneider, Y. Nevmyvaka, M. Tao, W. Deng. Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization. 2025
H. Zheng, X. Liu, X. Kong, N. Jiang, Z. Hu, W. Luo, W. Deng, G. Lin. Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct. 2025.

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.

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]