12 citations · 58 across the 15 of their papers we have counts for
16 papers
Adaptive Robust Confidence Sets in High Dimensions
Ilias Diakonikolas, Chao Gao, Daniel M. Kane +1
We study adaptive confidence sets for mean vectors of multivariate Gaussians with identity covariance under Huber contamination when the contamination proportion is unknown. We sho…
Computational Lower Bounds for Graphon Estimation via Low-degree Polynomials
Yuetian Luo, Chao Gao
Graphon estimation has been one of the most fundamental problems in network analysis and has received considerable attention in the past decade. From the statistical perspective, t…
Iterative Approximate Cross-Validation
Yuetian Luo, Zhimei Ren, Rina Foygel Barber
Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models. However, standard CV suffers from high computational cost when the number of f…
Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective
Yuetian Luo, Nicolas Garcia Trillos
We study a general matrix optimization problem with a fixed-rank positive semidefinite (PSD) constraint. We perform the Burer-Monteiro factorization and consider a particular Riema…
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
Yuetian Luo, Anru R. Zhang
We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without the prior knowle…
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization
Yuetian Luo, Xudong Li, Anru R. Zhang
In this paper, we propose a general procedure for establishing the geometric landscape connections of a Riemannian optimization problem under the embedded and quotient geometries.…
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
Yuetian Luo, Xudong Li, Anru R. Zhang
In this paper, we consider the geometric landscape connection of the widely studied manifold and factorization formulations in low-rank positive semidefinite (PSD) and general matr…
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence
Yuetian Luo, Anru R. Zhang
In this paper, we consider the estimation of a low Tucker rank tensor from a number of noisy linear measurements. The general problem covers many specific examples arising from app…
Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit
Rungang Han, Yuetian Luo, Miaoyan Wang +1
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex a…
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
Yuetian Luo, Wen Huang, Xudong Li +1
In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \…
Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection
Yuetian Luo, Anru R. Zhang
We note the significance of hypergraphic planted clique (HPC) detection in the investigation of computational hardness for a range of tensor problems. We ask if more evidence for t…
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
Yuetian Luo, Garvesh Raskutti, Ming Yuan +1
In this paper, we develop novel perturbation bounds for the high-order orthogonal iteration (HOOI) [DLDMV00b]. Under mild regularity conditions, we establish blockwise tensor pertu…
A Schatten- Low-rank Matrix Perturbation Analysis via Perturbation Projection Error Bound
Yuetian Luo, Rungang Han, Anru R. Zhang
This paper studies the Schatten- error of low-rank matrix estimation by singular value decomposition under perturbation. We specifically establish a perturbation bound on the lo…
Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits
Yuetian Luo, Anru R. Zhang
This paper studies the statistical and computational limits of high-order clustering with planted structures. We focus on two clustering models, constant high-order clustering (CHC…
ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
Anru Zhang, Yuetian Luo, Garvesh Raskutti +1
In this paper, we develop a novel procedure for low-rank tensor regression, namely \emph{\underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation fo…
Model Confidence Bounds for Variable Selection
Yang Li, Yuetian Luo, Davide Ferrari +2
In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confid…