1.1k citations · 1.7k across the 21 of their papers we have counts for
22 papers
Improving the scalability of Gaussian-process error marginalization in gravitational-wave inference
Miaoxin Liu, Xiao-Dong Li, Alvin J. K. Chua
The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally exp…
Learning Linear Polytree Structural Equation Models
Xingmei Lou, Yu Hu, Xiaodong Li
We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) and the causal structure can be…
Consistency of Spectral Clustering on Hierarchical Stochastic Block Models
Lihua Lei, Xiaodong Li, Xingmei Lou
We study the hierarchy of communities in real-world networks under a generic stochastic block model, in which the connection probabilities are structured in a binary tree. Under su…
Nonconvex Matrix Completion with Linearly Parameterized Factors
Ji Chen, Xiaodong Li, Zongming Ma
Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones. In practice including collaborative filt…
Nonconvex Rectangular Matrix Completion via Gradient Descent without Regularization
Ji Chen, Dekai Liu, Xiaodong Li
The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on…
Convex Relaxation Methods for Community Detection
Xiaodong Li, Yudong Chen, Jiaming Xu
This paper surveys recent theoretical advances in convex optimization approaches for community detection. We introduce some important theoretical techniques and results for establi…
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA
Ji Chen, Xiaodong Li
This work studies low-rank approximation of a positive semidefinite matrix from partial entries via nonconvex optimization. We characterized how well local-minimum based low-rank f…
When Do Birds of a Feather Flock Together? k-Means, Proximity, and Conic Programming
Xiaodong Li, Yang Li, Shuyang Ling +2
Given a set of data, one central goal is to group them into clusters based on some notion of similarity between the individual objects. One of the most popular and widely-used appr…
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
Xiaodong Li, Shuyang Ling, Thomas Strohmer +1
We study the question of reconstructing two signals and from their convolution . This problem, known as {\em blind deconvolution}, pervades many areas of scien…
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates
Zhuang Ma, Xiaodong Li
Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables. In this paper, motivated by rec…
Convexified Modularity Maximization for Degree-corrected Stochastic Block Models
Yudong Chen, Xiaodong Li, Jiaming Xu
The stochastic block model (SBM) is a popular framework for studying community detection in networks. This model is limited by the assumption that all nodes in the same community a…
Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow
T. Tony Cai, Xiaodong Li, Zongming Ma
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal from noisy quadratic measurements , $j=1, \l…
Global testing against sparse alternatives in time-frequency analysis
T. Tony Cai, Yonina C. Eldar, Xiaodong Li
In this paper, an over-sampled periodogram higher criticism (OPHC) test is proposed for the global detection of sparse periodic effects in a complex-valued time series. An explicit…
Phase Retrieval via Wirtinger Flow: Theory and Algorithms
Emmanuel Candes, Xiaodong Li, Mahdi Soltanolkotabi
We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x of C^n about which we have phaseless sample…
Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
T. Tony Cai, Xiaodong Li
Community detection, which aims to cluster nodes in a given graph into distinct groups based on the observed undirected edges, is an important problem in network data analy…
Phase Retrieval from Coded Diffraction Patterns
Emmanuel Candes, Xiaodong Li, Mahdi Soltanolkotabi
This paper considers the question of recovering the phase of an object from intensity-only measurements, a problem which naturally appears in X-ray crystallography and related disc…
Sparse Signal Recovery from Quadratic Measurements via Convex Programming
Xiaodong Li, Vladislav Voroninski
In this paper we consider a system of quadratic equations |<z_j, x>|^2 = b_j, j = 1, ..., m, where x in R^n is unknown while normal random vectors z_j in R_n and quadratic measurem…
Solving Quadratic Equations via PhaseLift when There Are About As Many Equations As Unknowns
Emmanuel J. Candes, Xiaodong Li
This note shows that we can recover a complex vector x in C^n exactly from on the order of n quadratic equations of the form |<a_i, x>|^2 = b_i, i = 1, ..., m, by using a semidefin…
Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions
Xiaodong Li
We improve existing results in the field of compressed sensing and matrix completion when sampled data may be grossly corrupted. We introduce three new theorems. 1) In compressed s…
Dense Error Correction for Low-Rank Matrices via Principal Component Pursuit
Arvind Ganesh, John Wright, Xiaodong Li +2
We consider the problem of recovering a low-rank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It…
Stable Principal Component Pursuit
Zihan Zhou, Xiaodong Li, John Wright +2
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross spa…
Robust Principal Component Analysis?
Emmanuel J. Candes, Xiaodong Li, Yi Ma +1
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component in…