Publications

I just list selected publication here. For a full list, you can view on my lab homepage and my Google Scholar profile.

Selected Publication

• Zexi Fan, Yan Sun, Shihao Yang, Yiping Lu Physics-Informed Inference Time Scaling via Simulation-Calibrated Scientific Machine Learning, submitted, 2025
• Ruihan Xu, Yiping Lu. What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation?, submitted, 2025
• Ruihan Xu, Yiping Lu. Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability, Artificial Intelligence and Statistics (AISTATS), 2025
• Yiping Lu, Daozhe Lin, Qiang Du. Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy, Submitted
• Yiping Lu, Jiajin Li, Lexing Ying, Jose Blancet. Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls, 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024) [Oral].
• Haotian Ye, Ruichen Li, Yuntian Gu, Yiping Lu, Di He, Liwei Wang. $O(N^2)$ Representation of General Continuous Anti-symmetric Function, submitted, 2024.
• Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos, Volkan Cevher. Generalization Guarantees of Deep ResNets in the Mean-Field Regime, International Conference on Learning Representations(ICLR) 2024, [Spotlight]
• 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
• Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying Minimax Optimal Kernel Operator Learning via Multilevel Training Eleventh International Conference on Learning Representations(ICLR) 2023 [Spotlight]
• 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
• 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
• 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 *, 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