67 citations · 166 across the 20 of their papers we have counts for
22 papers
The Decaying Missing-at-Random Framework: Model Doubly Robust Causal Inference with Partially Labeled Data
Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
In modern large-scale observational studies, data collection constraints often result in partially labeled datasets, posing challenges for reliable causal inference, especially due…
A Geometric Approach to -means
Jiazhen Hong, Wei Qian, Yudong Chen +1
\kmeans clustering is a fundamental problem in many scientific and engineering domains. The optimization problem associated with \kmeans clustering is nonconvex, for which standard…
Dynamic treatment effects: high-dimensional inference under model misspecification
Yuqian Zhang, Weijie Ji, Jelena Bradic
Estimating dynamic treatment effects is crucial across various disciplines, providing insights into the time-dependent causal impact of interventions. However, this estimation pose…
High-dimensional Inference for Dynamic Treatment Effects
Jelena Bradic, Weijie Ji, Yuqian Zhang
Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have e…
Unique sparse decomposition of low rank matrices
Dian Jin, Xin Bing, Yuqian Zhang
The problem of finding the unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of…
Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap
Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
Semi-supervised (SS) inference has received much attention in recent years. Apart from a moderate-sized labeled data, L, the SS setting is characterized by an additional, much larg…
Local Minima Structures in Gaussian Mixture Models
Yudong Chen, Dogyoon Song, Xumei Xi +1
We investigate the landscape of the negative log-likelihood function of Gaussian Mixture Models (GMMs) with a general number of components in the population limit. As the objective…
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle
Lijun Ding, Yuqian Zhang, Yudong Chen
Existing results for low-rank matrix recovery largely focus on quadratic loss, which enjoys favorable properties such as restricted strong convexity/smoothness (RSC/RSM) and well c…
From Symmetry to Geometry: Tractable Nonconvex Problems
Yuqian Zhang, Qing Qu, John Wright
As science and engineering have become increasingly data-driven, the role of optimization has expanded to touch almost every stage of the data analysis pipeline, from signal and da…
Structures of Spurious Local Minima in -means
Wei Qian, Yuqian Zhang, Yudong Chen
-means clustering is a fundamental problem in unsupervised learning. The problem concerns finding a partition of the data points into clusters such that the within-cluster v…
Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning
Jicong Fan, Yuqian Zhang, Madeleine Udell
This paper develops new methods to recover the missing entries of a high-rank or even full-rank matrix when the intrinsic dimension of the data is low compared to the ambient dimen…
Analysis of the Optimization Landscapes for Overcomplete Representation Learning
Qing Qu, Yuexiang Zhai, Xiao Li +2
We study nonconvex optimization landscapes for learning overcomplete representations, including learning (i) sparsely used overcomplete dictionaries and (ii) convolutional dictiona…
Short-and-Sparse Deconvolution -- A Geometric Approach
Yenson Lau, Qing Qu, Han-Wen Kuo +3
Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring motifs in signals with spatial or temporal structure. Variants of this problem arise in appl…
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities
Wei Qian, Yuqian Zhang, Yudong Chen
This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm…
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution
Yuqian Zhang, Yenson Lau, Han-Wen Kuo +3
Blind deconvolution is the problem of recovering a convolutional kernel and an activation signal from their convolution $\boldsymbol y = \boldsy…
Geometry and Symmetry in Short-and-Sparse Deconvolution
Han-Wen Kuo, Yenson Lau, Yuqian Zhang +1
We study the problem of recovering a short signal and a sparse signal from their convolution. We propose…
Dictionary Learning in Fourier Transform Scanning Tunneling Spectroscopy
Sky C. Cheung, John Y. Shin, Yenson Lau +5
Modern high-resolution microscopes, such as the scanning tunneling microscope, are commonly used to study specimens that have dense and aperiodic spatial structure. Extracting mean…
Structured Local Optima in Sparse Blind Deconvolution
Yuqian Zhang, Han-Wen Kuo, John Wright
Blind deconvolution is a ubiquitous problem of recovering two unknown signals from their convolution. Unfortunately, this is an ill-posed problem in general. This paper focuses on…
Convolutional Phase Retrieval via Gradient Descent
Qing Qu, Yuqian Zhang, Yonina C. Eldar +1
We study the convolutional phase retrieval problem, of recovering an unknown signal from measurements consisting of the magnitude of its cyclic con…
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
Cun Mu, Yuqian Zhang, John Wright +1
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learnin…
Toward Guaranteed Illumination Models for Non-Convex Objects
Yuqian Zhang, Cun Mu, Han-wen Kuo +1
Illumination variation remains a central challenge in object detection and recognition. Existing analyses of illumination variation typically pertain to convex, Lambertian objects,…
Efficient Point-to-Subspace Query in : Theory and Applications in Computer Vision
Ju Sun, Yuqian Zhang, John Wright
Motivated by vision tasks such as robust face and object recognition, we consider the following general problem: given a collection of low-dimensional linear subspaces in a high-di…