139 citations · 725 across the 72 of their papers we have counts for
17 papers · 1 filter
Power Iteration for Tensor PCA
Jiaoyang Huang, Daniel Z. Huang, Qing Yang +1
In this paper, we study the power iteration algorithm for the spiked tensor model, as introduced in [44]. We give necessary and sufficient conditions for the convergence of the pow…
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…
Union-net: A deep neural network model adapted to small data sets
Jingyi Zhou, Qingfang He, Zhiying Lin
In real applications, generally small data sets can be obtained. At present, most of the practical applications of machine learning use classic models based on big data to solve th…
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…