You can also find my articles on my Google Scholar profile.
Jose Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying. When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality (alphabetical order) Thirty-seventh Conference on Neural Information Processing Systems (Neurips) 2023
Yiping Lu, Jiajin Li, Lexing Ying, Jose Blancet. Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls.
Yiping Lu , Wenlong Ji, Zach Izzo,Lexing Ying. Importance Tempering: Group Robustness for Overparameterized Models. Submitted (equal contribution)
Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying Minimax Optimal Kernel Operator Learning via Multilevel Training Eleventh International Conference on Learning Representations(ICLR) 2023
Huishuai Zhang, Da yu, Yiping Lu, Di He. Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023
Yiping Lu, Jose Blanchet,Lexing Ying. Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022
Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose Blanchet. Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. 10th International Conference on Learning Representations(ICLR) 2022
Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J Su. An Unconstrained Layer-Peeled Perspective on Neural Collapse. Tenth International Conference on Learning Representations(ICLR) 2022
Bin Dong, Haochen Ju, Yiping Lu, Zuoqiang Shi. “ CURE: Curvature Regularization For Missing Data Recovery.” SIAM Journal on Imaging Science, 13(4), 2169-2188, 2020
Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying. “A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth” Thirty-seventh International Conference on Machine Learning (ICML), 2020
Yiping Lu , Zhuohan Li, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-yan Liu “Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View.” (*equal contribution) ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations.
Bin Dong, Jikai Hou, Yiping Lu, Zhihua Zhang “Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network” NeurIPS2019 Workshop on ML with Guarantees. (alphabetical ordering)
Dinghuai Zhang, Tianyuan Zhang, Yiping Lu , Zhanxing Zhu, Bin Dong. “You Only Propagate Once: Painless Adversarial Training Using Maximal Principle” (equal contribution) 33rd Annual Conference on Neural Information Processing Systems (NeurIPS) 2019.
Zichao Long, Yiping Lu, Bin Dong. “ PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network” Journal of Computational Physics, 399, 108925, 2019.
Xiaoshuai Zhang, Yiping Lu *, Jiaying Liu, Bin Dong. “Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration” Seventh International Conference on Learning Representations(ICLR) 2019(equal contribution)
Zichao long, Yiping Lu *, Xianzhong Ma, Bin Dong. “PDE-Net:Learning PDEs From Data”, Thirty-fifth International Conference on Machine Learning (ICML), 2018(*equal contribution)
Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong. “Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical Differential Equations” Thirty-fifth International Conference on Machine Learning (ICML), 2018