Publications

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