37 citations · 76 across the 20 of their papers we have counts for
21 papers
Randomized Submanifold Subgradient Method for Optimization over Stiefel Manifolds
Andy Yat-Ming Cheung, Jinxin Wang, Man-Chung Yue +1
Optimization over the Stiefel manifold is a fundamental computational problem in many scientific and engineering applications. Despite considerable research effort, high-dimensiona…
Approximate Secular Equations for the Cubic Regularization Subproblem
Yihang Gao, Man-Chung Yue, Michael K. Ng
The cubic regularization method (CR) is a popular algorithm for unconstrained non-convex optimization. At each iteration, CR solves a cubically regularized quadratic problem, calle…
Robust Bayesian Recourse
Tuan-Duy H. Nguyen, Ngoc Bui, Duy Nguyen +2
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnos…
Variance Reduced Random Relaxed Projection Method for Constrained Finite-sum Minimization Problems
Zhichun Yang, Fu-quan Xia, Kai Tu +1
For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In t…
Distributionally Robust Fair Principal Components via Geodesic Descents
Hieu Vu, Toan Tran, Man-Chung Yue +1
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, hea…
Short-step Methods Are Not Strongly Polynomial-Time
Manru Zong, Yin Tat Lee, Man-Chung Yue
Short-step methods are an important class of algorithms for solving convex constrained optimization problems. In this short paper, we show that under very mild assumptions on the s…
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man-Chung Yue, Jose Blanchet +2
Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additi…
Small errors in random zeroth-order optimization are imaginary
Wouter Jongeneel, Man-Chung Yue, Daniel Kuhn
Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a…
A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group
Huikang Liu, Man-Chung Yue, Anthony Man-Cho So
The problem of synchronization over a group aims to estimate a collection of group elements based on noisy observations of a sub…
A Matrix Generalization of the Hardy-Littlewood-Pólya Rearrangement Inequality and Its Applications
Man-Chung Yue
By analyzing an optimization problem over orthogonal matrices, we prove a generalization of the Hardy-Littlewood-Pólya rearrangement inequality to positive definite matrices. The i…
On Linear Optimization over Wasserstein Balls
Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distribut…
An accelerated first-order method with complexity analysis for solving cubic regularization subproblems
Rujun Jiang, Man-Chung Yue, Zhishuo Zhou
We propose a first-order method to solve the cubic regularization subproblem (CRS) based on a novel reformulation. The reformulation is a constrained convex optimization problem wh…
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications.…
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue +2
A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these…
Universal Barrier is -Self-Concordant
Yin Tat Lee, Man-Chung Yue
This paper shows that the self-concordance parameter of the universal barrier on any -dimensional proper convex domain is upper bounded by . This bound is tight and improves…
Phase Retrieval via Sensor Network Localization
Sherry Xue-Ying Ni, Man-Chung Yue, Kam-Fung Cheung +1
The problem of phase retrieval is revisited and studied from a fresh perspective. In particular, we establish a connection between the phase retrieval problem and the sensor networ…
On the Quadratic Convergence of the Cubic Regularization Method under a Local Error Bound Condition
Man-Chung Yue, Zirui Zhou, Anthony Man-Cho So
In this paper we consider the cubic regularization (CR) method for minimizing a twice continuously differentiable function. While the CR method is widely recognized as a globally c…
A Family of Inexact SQA Methods for Non-Smooth Convex Minimization with Provable Convergence Guarantees Based on the Luo-Tseng Error Bound Property
Man-Chung Yue, Zirui Zhou, Anthony Man-Cho So
We propose a new family of inexact sequential quadratic approximation (SQA) methods, which we call the inexact regularized proximal Newton () method, for minimizing…
On the Estimation Performance and Convergence Rate of the Generalized Power Method for Phase Synchronization
Huikang Liu, Man-Chung Yue, Anthony Man-Cho So
An estimation problem of fundamental interest is that of phase synchronization, in which the goal is to recover a collection of phases using noisy measurements of relative phases.…
A Robust Design for MISO Physical-Layer Multicasting over Line-of-Sight Channels
Man-Chung Yue, Sissi Xiaoxiao Wu, Anthony Man-Cho So
This paper studies a robust design problem for far-field line-of-sight (LOS) channels where phase errors are present. Compared with the commonly used additive error model, the phas…
A Perturbation Inequality for the Schatten- Quasi-Norm and Its Applications to Low-Rank Matrix Recovery
Man-Chung Yue, Anthony Man-Cho So
In this paper, we establish the following perturbation result concerning the singular values of a matrix: Let be given matrices, and let $f:\mathbb…