research
∙
08/04/2023
Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models
This paper develops an inferential framework for matrix completion when ...
share
research
∙
07/02/2023
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
This paper introduces a novel framework called Mode-wise Principal Subsp...
research
∙
03/31/2023
Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractability
In this paper, we investigate the optimal statistical performance and th...
research
∙
02/14/2023
On the Multiway Principal Component Analysis
Multiway data are becoming more and more common. While there are many ap...
research
∙
11/11/2021
On Recovering the Best Rank-r Approximation from Few Entries
In this note, we investigate how well we can reconstruct the best rank-r...
research
∙
09/29/2021
Assessing the goodness of fit of linear regression via higher-order least squares
We introduce a simple diagnostic test for assessing the goodness of fit ...
research
∙
07/20/2021
On Estimating Rank-One Spiked Tensors in the Presence of Heavy Tailed Errors
In this paper, we study the estimation of a rank-one spiked tensor in th...
research
∙
02/07/2021
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Fairness in AI and machine learning systems has become a fundamental pro...
research
∙
12/10/2020
Detecting Structured Signals in Ising Models
In this paper, we study the effect of dependence on detecting a class of...
research
∙
08/06/2020
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
In this paper, we develop novel perturbation bounds for the high-order o...
research
∙
07/17/2020
Perturbation Bounds for Orthogonally Decomposable Tensors and Their Applications in High Dimensional Data Analysis
We develop deterministic perturbation bounds for singular values and vec...
research
∙
11/09/2019
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
In this paper, we develop a novel procedure for low-rank tensor regressi...
research
∙
09/07/2019
On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives
Nonparametric tests via kernel embedding of distributions have witnessed...
research
∙
08/31/2019
Statistical Inferences of Linear Forms for Noisy Matrix Completion
We introduce a flexible framework for making inferences about general li...
research
∙
02/13/2018
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network
Image demosaicing - one of the most important early stages in digital ca...
research
∙
11/20/2017
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Recent results in coupled or temporal graphical models offer schemes for...
research
∙
11/14/2017
Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries
In this article, we develop methods for estimating a low rank tensor fro...
research
∙
10/31/2017
Effective Tensor Sketching via Sparsification
In this paper, we investigate effective sketching schemes via sparsifica...
research
∙
09/24/2017
On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests
The reproducing kernel Hilbert space (RKHS) embedding of distributions o...
research
∙
02/22/2017
On Polynomial Time Methods for Exact Low Rank Tensor Completion
In this paper, we investigate the sample size requirement for exact reco...
research
∙
06/10/2016
Incoherent Tensor Norms and Their Applications in Higher Order Tensor Completion
In this paper, we investigate the sample size requirement for a general ...
research
∙
03/10/2015
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition
We establish minimax optimal rates of convergence for estimation in a hi...
research
∙
07/10/2014
Rate-Optimal Detection of Very Short Signal Segments
Motivated by a range of applications in engineering and genomics, we con...
research
∙
05/07/2014
On Tensor Completion via Nuclear Norm Minimization
Many problems can be formulated as recovering a low-rank tensor. Althoug...
research
∙
06/27/2012
The Nonparanormal SKEPTIC
We propose a semiparametric approach, named nonparanormal skeptic, for e...
research
∙
02/10/2012
High Dimensional Semiparametric Gaussian Copula Graphical Models
In this paper, we propose a semiparametric approach, named nonparanormal...
research
∙
10/02/2007
