M-estimation in Low-Rank Matrix Factorization: A General Framework. - Wei Tu; Peng Liu 048; Jingyu Zhao 001; Yi Liu 062; Linglong Kong; Guodong Li; Bei Jiang; Guangjian Tian; Hengshuai Yao (ICDM, 2019)

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. - Tong Su; Yafei Wang; Yi Liu 062; William G. Branton; Eugene Asahchop; Christopher Power; Bei Jiang; Linglong Kong; Niansheng Tang (Entropy, 2020)

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. - Ke Sun 013; Yafei Wang; Yi Liu 062; Yingnan Zhao; Bo Pan; Shangling Jui; Bei Jiang; Linglong Kong (NeurIPS, 2021)

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. - Ke Sun 013; Yi Liu 062; Yingnan Zhao; Hengshuai Yao; Shangling Jui; Linglong Kong (CoRR, 2021)

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Enze Shi; Yafei Wang; Aref Sadeghi; Xiaodong Yan; Bei Jiang; Linglong Kong (CoRR, 2021)

Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. - Yi Liu 062; Ke Sun 013; Bei Jiang; Linglong Kong (NeurIPS, 2022)

Distributional Reinforcement Learning via Sinkhorn Iterations. - Ke Sun 013; Yingnan Zhao; Yi Liu 062; Bei Jiang; Linglong Kong (CoRR, 2022)

Optimal Smooth Approximation for Quantile Matrix Factorization. - Peng Liu 048; Yi Liu 062; Rui Zhu 007; Linglong Kong; Bei Jiang; Di Niu (SDM, 2023)