Yiping Lu | DeepAI

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Featured Co-authors

Tie-Yan Liu
176 publications
Liwei Wang
155 publications
Tao Qin
134 publications
Jianfeng Lu
98 publications
Chao Ma
82 publications
Di He
71 publications
Jiaying Liu
70 publications
Lexing Ying
69 publications
Bin Dong
61 publications
Jose Blanchet
56 publications
Zhihua Zhang
53 publications

∙ 05/25/2023

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

This paper studies the use of a machine learning-based estimator as a co...

0 Jose Blanchet, et al. ∙

∙ 11/28/2022

Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls

The optimal design of experiments typically involves solving an NP-hard ...

0 Yiping Lu, et al. ∙

∙ 09/28/2022

Minimax Optimal Kernel Operator Learning via Multilevel Training

Learning mappings between infinite-dimensional function spaces has achie...

7 Jikai Jin, et al. ∙

∙ 09/19/2022

Importance Tempering: Group Robustness for Overparameterized Models

Although overparameterized models have shown their success on many machi...

1 Yiping Lu, et al. ∙

∙ 06/09/2022

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

Adversarial examples, which are usually generated for specific inputs wi...

0 Huishuai Zhang, et al. ∙

∙ 05/15/2022

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

In this paper, we study the statistical limits in terms of Sobolev norms...

11 Yiping Lu, et al. ∙

∙ 10/13/2021

Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality

In this paper, we study the statistical limits of deep learning techniqu...

21 Yiping Lu, et al. ∙

∙ 10/06/2021

An Unconstrained Layer-Peeled Perspective on Neural Collapse

Neural collapse is a highly symmetric geometric pattern of neural networ...

0 Wenlong Ji, et al. ∙

∙ 03/11/2020

A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth

Training deep neural networks with stochastic gradient descent (SGD) can...

42 Yiping Lu, et al. ∙

∙ 10/02/2019

Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network

Distillation is a method to transfer knowledge from one model to another...

0 Bin Dong, et al. ∙

∙ 06/06/2019

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

The Transformer architecture is widely used in natural language processi...

0 Yiping Lu, et al. ∙

∙ 05/02/2019

You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle

Deep learning achieves state-of-the-art results in many areas. However r...

0 Dinghuai Zhang, et al. ∙

∙ 05/02/2019

You Only Propagate Once: Painless Adversarial Training Using Maximal Principle

Deep learning achieves state-of-the-art results in many areas. However r...

0 Dinghuai Zhang, et al. ∙

∙ 01/28/2019

CURE: Curvature Regularization For Missing Data Recovery

Missing data recovery is an important and yet challenging problem in ima...

0 Bin Dong, et al. ∙

∙ 11/30/2018

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

Partial differential equations (PDEs) are commonly derived based on empi...

0 Zichao Long, et al. ∙

∙ 05/20/2018

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

In this paper, we propose a new control framework called the moving endp...

0 Xiaoshuai Zhang, et al. ∙

∙ 10/27/2017

Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

In our work, we bridge deep neural network design with numerical differe...

0 Yiping Lu, et al. ∙

∙ 10/26/2017

PDE-Net: Learning PDEs from Data

In this paper, we present an initial attempt to learn evolution PDEs fro...

0 Zichao Long, et al. ∙

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