Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. - Kaixuan Huang; Yuqing Wang 005; Molei Tao; Tuo Zhao (NeurIPS, 2020)

Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. - Yuqing Wang 005; Minshuo Chen; Tuo Zhao; Molei Tao (ICLR, 2022)

Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport. - Lingkai Kong; Yuqing Wang 005; Molei Tao (ICLR, 2023)

Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult. - Yuqing Wang 005; Zhenghao Xu; Tuo Zhao; Molei Tao (CoRR, 2023)

Markov Chain Monte Carlo for Gaussian: A Linear Control Perspective. - Bo Yuan 016; Jiaojiao Fan; Yuqing Wang 005; Molei Tao; Yongxin Chen 002 (IEEE Control. Syst. Lett., 2023)

Evaluating the design space of diffusion-based generative models. - Yuqing Wang 005; Ye He 003; Molei Tao (NeurIPS, 2024)

Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks. - Zhenghao Xu; Yuqing Wang 005; Tuo Zhao; Rachel Ward; Molei Tao (NeurIPS, 2024)

Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks. - Zhenghao Xu; Yuqing Wang 005; Tuo Zhao; Rachel Ward; Molei Tao (CoRR, 2024)

Quantitative convergence analysis of dynamical processes in machine learning. - Yuqing Wang 005 (2024)