139 citations · 725 across the 72 of their papers we have counts for
74 papers
Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm
Huiming Zhang, Haoyu Wei, Guang Cheng
In non-asymptotic learning, variance-type parameters of sub-Gaussian distributions are of paramount importance. However, directly estimating these parameters using the empirical mo…
Benefit of Interpolation in Nearest Neighbor Algorithms
Yue Xing, Qifan Song, Guang Cheng
In some studies \citep[e.g.,][]{zhang2016understanding} of deep learning, it is observed that over-parametrized deep neural networks achieve a small testing error even when the tra…
Decentralized Sparse Linear Regression via Gradient-Tracking: Linear Convergence and Statistical Guarantees
Marie Maros, Gesualdo Scutari, Ying Sun +1
We study sparse linear regression over a network of agents, modeled as an undirected graph and no server node. The estimation of the -sparse parameter is formulated as a constra…
Online Bootstrap Inference For Policy Evaluation in Reinforcement Learning
Pratik Ramprasad, Yuantong Li, Zhuoran Yang +3
The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing m…
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality
Yang Yu, Shih-Kang Chao, Guang Cheng
We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces an $\ell…
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…