139 citations · 725 across the 72 of their papers we have counts for
12 papers · 2 filters
Adversarially Robust Estimate and Risk Analysis in Linear Regression
Yue Xing, Ruizhi Zhang, Guang Cheng
Adversarially robust learning aims to design algorithms that are robust to small adversarial perturbations on input variables. Beyond the existing studies on the predictive perform…
Online Forgetting Process for Linear Regression Models
Yuantong Li, Chi-hua Wang, Guang Cheng
Motivated by the EU's "Right To Be Forgotten" regulation, we initiate a study of statistical data deletion problems where users' data are accessible only for a limited period of ti…
Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee
Jincheng Bai, Qifan Song, Guang Cheng
Sparse deep learning aims to address the challenge of huge storage consumption by deep neural networks, and to recover the sparse structure of target functions. Although tremendous…
Nearly Optimal Variational Inference for High Dimensional Regression with Shrinkage Priors
Jincheng Bai, Qifan Song, Guang Cheng
We propose a variational Bayesian (VB) procedure for high-dimensional linear model inferences with heavy tail shrinkage priors, such as student-t prior. Theoretically, we establish…
On the Generalization Properties of Adversarial Training
Yue Xing, Qifan Song, Guang Cheng
Modern machine learning and deep learning models are shown to be vulnerable when testing data are slightly perturbed. Existing theoretical studies of adversarial training algorithm…
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Tianyang Hu, Wenjia Wang, Cong Lin +1
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data. However, the generalization guarantee may not hold for noisy data. From a…
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing
Chi-Hua Wang, Zhanyu Wang, Will Wei Sun +1
Devising dynamic pricing policy with always valid online statistical learning procedure is an important and as yet unresolved problem. Most existing dynamic pricing policy, which f…
Online Batch Decision-Making with High-Dimensional Covariates
Chi-Hua Wang, Guang Cheng
We propose and investigate a class of new algorithms for sequential decision making that interacts with \textit{a batch of users} simultaneously instead of \textit{a user} at each…
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu, Shih-Kang Chao, Guang Cheng
In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we…
Residual Bootstrap Exploration for Bandit Algorithms
Chi-Hua Wang, Yang Yu, Botao Hao +1
In this paper, we propose a novel perturbation-based exploration method in bandit algorithms with bounded or unbounded rewards, called residual bootstrap exploration (\texttt{ReBoo…
Predictive Power of Nearest Neighbors Algorithm under Random Perturbation
Yue Xing, Qifan Song, Guang Cheng
We consider a data corruption scenario in the classical Nearest Neighbors (-NN) algorithm, that is, the testing data are randomly perturbed. Under such a scenario, the impac…
Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting
Tianyang Hu, Zuofeng Shang, Guang Cheng
Classifiers built with neural networks handle large-scale high dimensional data, such as facial images from computer vision, extremely well while traditional statistical methods of…