| 2022 | COLT | ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. | Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan |
| 2022 | COLT | Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. | Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett |
| 2020 | COLT | On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration. | Wenlong Mou, Chris Junchi Li, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan |
| 2018 | COLT | Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints. | Wenlong Mou, Liwei Wang, Xiyu Zhai, Kai Zheng |
| 2018 | ICML | Dropout Training, Data-dependent Regularization, and Generalization Bounds. | Wenlong Mou, Yuchen Zhou, Jun Gao, Liwei Wang |
| 2017 | ICML | Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible. | Kai Zheng, Wenlong Mou, Liwei Wang |
| 2017 | ICML | Differentially Private Clustering in High-Dimensional Euclidean Spaces. | Maria-Florina Balcan, Travis Dick, Yingyu Liang, Wenlong Mou, Hongyang Zhang |
| 2017 | IJCAI | Efficient Private ERM for Smooth Objectives. | Jiaqi Zhang, Kai Zheng, Wenlong Mou, Liwei Wang |