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ORCIDYiping Lu 0001
Person information
- affiliation: Stanford University, Institute for Computational and Mathematical Engineering, ICME, CA, USA
- affiliation: Peking University, School of Mathematical Sciences, Beijing, China
Other persons with the same name
- Yiping Lu — disambiguation page
- Yiping Lu 0002 — Beijing Jiaotong University, School of Mechanical, Electronic and Control Engineering, China
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2020 – today
- 2024
[c15]Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff:
Statistical Spatially Inhomogeneous Diffusion Inference. AAAI 2024: 14820-14828- [c14]Kaizhao Liu, José H. Blanchet, Lexing Ying, Yiping Lu:
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. ICML 2024 - [i19]Kaizhao Liu, Jose H. Blanchet, Lexing Ying, Yiping Lu:
Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty. CoRR abs/2404.19145 (2024) - 2023
- [c13]Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. AISTATS 2023: 2792-2804 - [c12]Jikai Jin, Yiping Lu, José H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. ICLR 2023 - [c11]Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying:
When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality. NeurIPS 2023 - [i18]Jose H. Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying:
When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality. CoRR abs/2305.16527 (2023) - [i17]Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff:
Statistical Spatially Inhomogeneous Diffusion Inference. CoRR abs/2312.05793 (2023) - 2022
- [c10]Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose H. Blanchet:
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. ICLR 2022 - [c9]Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J. Su:
An Unconstrained Layer-Peeled Perspective on Neural Collapse. ICLR 2022 - [c8]Yiping Lu, José H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. NeurIPS 2022 - [i16]Yiping Lu, Jose H. Blanchet, Lexing Ying:
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent. CoRR abs/2205.07331 (2022) - [i15]Huishuai Zhang, Da Yu, Yiping Lu, Di He:
Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks. CoRR abs/2206.04316 (2022) - [i14]Yiping Lu, Wenlong Ji, Zachary Izzo, Lexing Ying:
Importance Tempering: Group Robustness for Overparameterized Models. CoRR abs/2209.08745 (2022) - [i13]Jikai Jin, Yiping Lu, Jose H. Blanchet, Lexing Ying:
Minimax Optimal Kernel Operator Learning via Multilevel Training. CoRR abs/2209.14430 (2022) - [i12]Yiping Lu, Jiajin Li, Lexing Ying, Jose H. Blanchet:
Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls. CoRR abs/2211.15241 (2022) - 2021
- [i11]Wenlong Ji, Yiping Lu
, Yiliang Zhang, Zhun Deng, Weijie J. Su:
An Unconstrained Layer-Peeled Perspective on Neural Collapse. CoRR abs/2110.02796 (2021) - [i10]Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose H. Blanchet:
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality. CoRR abs/2110.06897 (2021) - 2020
- [j2]Bin Dong, Haocheng Ju, Yiping Lu, Zuoqiang Shi:
CURE: Curvature Regularization for Missing Data Recovery. SIAM J. Imaging Sci. 13(4): 2169-2188 (2020) - [c7]Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying:
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth. ICML 2020: 6426-6436 - [i9]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. CoRR abs/2003.05508 (2020)
2010 – 2019
- 2019
- [j1]Zichao Long, Yiping Lu, Bin Dong:
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network. J. Comput. Phys. 399 (2019) - [c6]Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong:
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration. ICLR (Poster) 2019 - [c5]Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. NeurIPS 2019: 227-238 - [i8]Bin Dong, Haocheng Ju, Yiping Lu, Zuoqiang Shi:
CURE: Curvature Regularization For Missing Data Recovery. CoRR abs/1901.09548 (2019) - [i7]Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong:
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. CoRR abs/1905.00877 (2019) - [i6]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. CoRR abs/1906.02762 (2019) - [i5]Bin Dong, Jikai Hou, Yiping Lu, Zhihua Zhang:
Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network. CoRR abs/1910.01255 (2019) - 2018
- [c4]Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong:
PDE-Net: Learning PDEs from Data. ICLR (Workshop) 2018 - [c3]Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong:
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. ICLR (Workshop) 2018 - [c2]Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong:
PDE-Net: Learning PDEs from Data. ICML 2018: 3214-3222 - [c1]Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong:
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. ICML 2018: 3282-3291 - [i4]Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong:
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration. CoRR abs/1805.07709 (2018) - [i3]Zichao Long, Yiping Lu, Bin Dong:
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network. CoRR abs/1812.04426 (2018) - 2017
- [i2]Zichao Long, Yiping Lu, Xianzhong Ma, Bin Dong:
PDE-Net: Learning PDEs from Data. CoRR abs/1710.09668 (2017) - [i1]Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong:
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations. CoRR abs/1710.10121 (2017)
Coauthor Index
aka: José H. Blanchet
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