Faculty Directory
Yiping Lu

Assistant Professor of Industrial Engineering and Management Sciences

Contact

2145 Sheridan Road
Tech M237
Evanston, IL 60208-3109

Email Yiping Lu

Website

Yiping Lu's Website


Departments

Industrial Engineering and Management Sciences



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Education

Ph.D ,Applied and computational math, Stanford 

B.S., Computational Math, Peking University


Research Interests

The long-term goal of Yiping’s research is to develop a hybrid scientific research discipline that combines domain knowledge (differential equation, stochastic process, control,…), machine learning/AI and (randomized) experiments. To this end, Yiping and his group are working on an interdisciplinary research approach across probability and statistics, machine learning, numerical algorithms, control theory, signal processing/inverse problem, and operations research. 


Our group (https://scale-lab-northwestern.github.io/) aim to build the foundation of the new generation of AI powered research paradigm that integrates scientific and engineering knowledge into Machine learning and Generative AI. We have a diverse and continuously expanding scope of research interests spanning the theoretical and algorithm development aspects of the new generation of AI powered research paradigm that integrates scientific and engineering knowledge into Machine learning and Generative AI. Our team is dedicated to research at the intersection of machine learning, non-parametric statistics, applied probability, and numerical analysis. Our primary focus lies in developing algorithmic and theoretical foundations for modern domains such as Deep LearningGenerative AI, and AI4Science.


Yiping is among the first to work at the interface of deep learning and differential equation. Yiping was a recipient of the Conference on Parsimony and Learning (CPAL) Rising Star Award in 2024, the Rising Star in Data Science from the University of Chicago in 2022, the Stanford Interdisciplinary Graduate Fellowship and the SenseTime Scholarship in 2021 for undergraduates in AI in 2019. Yiping also served as Area Chair for top machine learning conference such as ICML, Neurips and AISTATS.


Selected Publications

Bengin overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias

Honam Wong, Wendao Wu, Fanghui Liu and Yiping Lu

Which Spaces can be Embedded in Lp-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy

Yiping Lu, Daozhe Lin and Qiang Du

Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability

Ruihan Xu and Yiping Lu

Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty

Kaizhao Liu, Jose Blanchet, Lexing Ying and Yiping Lu The Forty-first International Conference on Machine Learning (ICML 2024).

Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls (Oral)

Yiping Lu, Jiajin Li, Lexing Ying and Jose Blanchet 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)

Generalization Guarantees of Deep ResNets in the Mean-Field Regime (Spotlight)

Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios Chrysos and Volkan Cevher International Conference on Learning Representations(ICLR) 2024

Statistical Spatially Inhomogeneous Diffusion Inference

Yinuo Ren, Yiping Lu, Lexing Ying and Grant Rotskoff The 38th Annual AAAI Conference on Artificial Intelligence, 2024

When can Regression-Adjusted Control Variates Help? Rare Events, Sobolev Embedding and Minimax Optimality

Jose Blanchet, Haoxuan Chen, Yiping Lu and Lexing Ying

Thirty-seventh Conference on Neural Information Processing Systems (Neurips) 2023

Minimax Optimal Kernel Operator Learning via Multilevel Training (Spotlight)

Jikai Jin, Yiping Lu, Jose Blanchet, Lexing Ying

Eleventh International Conference on Learning Representations(ICLR) 2023

Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks

Huishuai Zhang, Da yu, Yiping Lu and Di He

26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023

Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent

Yiping Lu, Jose Blanchet,Lexing Ying

Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) 2022

Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality (Spotlight Talk at NeurIPS 2021 Workshop on Integration of Deep Learning and Differential Equation)

Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose Blanchet

10th International Conference on Learning Representations(ICLR) 2022

An Unconstrained Layer-Peeled Perspective on Neural Collapse

Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J Su

Tenth International Conference on Learning Representations(ICLR) 2022

CURE: Curvature Regularization For Missing Data Recovery

Bin Dong, Haochen Ju, Yiping Lu, Zuoqiang Shi

SIAM Journal on Imaging Science, 13(4), 2169-2188, 2020

A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth (Contributed Talk at ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations.)

Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying

Thirty-seventh International Conference on Machine Learning (ICML), 2020

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View

Yiping Lu *, Zhuohan Li*, Di He, Zhiqing Sun, Bin Dong, Tao Qin, Liwei Wang, Tie-yan Liu

You Only Propagate Once: Painless Adversarial Training Using Maximal Principle

Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

33rd Annual Conference on Neural Information Processing Systems (NeurIPS) 2019

PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

Zichao Long, Yiping Lu, Bin Dong

Journal of Computational Physics

Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration

Xiaoshuai Zhang*, Yiping Lu , Jiaying Liu, Bin Dong.

Seventh International Conference on Learning Representations(ICLR) 2019

PDE-Net:Learning PDEs From Data

Zichao long*, Yiping Lu *, Xianzhong Ma*, Bin Dong

Thirty-fifth International Conference on Machine Learning (ICML), 2018

Beyond Finite Layer Neural Network:Bridging Deep Architects and Numerical Differential Equations

Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong.

Thirty-fifth International Conference on Machine Learning (ICML), 2018