9 citations · 22 across the 10 of their papers we have counts for
11 papers
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Haoyang Liu, Maheep Chaudhary, Haohan Wang
The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretab…
Augmented Lagrangian Methods for Time-varying Constrained Online Convex Optimization
Haoyang Liu, Xiantao Xiao, Liwei Zhang
In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based onl…
Regrets of Proximal Method of Multipliers for Online Non-convex Optimization with Long Term Constraints
Liwei Zhang, Haoyang Liu, Xiantao Xiao
The online optimization problem with non-convex loss functions over a closed convex set, coupled with a set of inequality (possibly non-convex) constraints is a challenging online…
A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming
Yifei Wang, Kangkang Deng, Haoyang Liu +1
We develop a decomposition method based on the augmented Lagrangian framework to solve a broad family of semidefinite programming problems, possibly with nonlinear objective functi…
Minimax rates in sparse, high-dimensional changepoint detection
Haoyang Liu, Chao Gao, Richard J. Samworth
We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing problem. Our first main contribution is to derive the exact minimax testing rate ac…
Exact high-dimensional asymptotics for Support Vector Machine
Haoyang Liu
The Support Vector Machine (SVM) is one of the most widely used classification methods. In this paper, we consider the soft-margin SVM used on data points with independent features…
Low-rank Matrix Optimization Using Polynomial-filtered Subspace Extraction
Yongfeng Li, Haoyang Liu, Zaiwen Wen +1
In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional firs…
An equivalence between critical points for rank constraints versus low-rank factorizations
Wooseok Ha, Haoyang Liu, Rina Foygel Barber
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so th…
Between hard and soft thresholding: optimal iterative thresholding algorithms
Haoyang Liu, Rina Foygel Barber
Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the obje…
Density Estimation with Contaminated Data: Minimax Rates and Theory of Adaptation
Haoyang Liu, Chao Gao
This paper studies density estimation under pointwise loss in the setting of contamination model. The goal is to estimate at some with i.i.d. observatio…
A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection
Junyu Zhang, Haoyang Liu, Zaiwen Wen +1
In this paper, we consider the community detection problem under either the stochastic block model (SBM) assumption or the degree-correlated stochastic block model (DCSBM) assumpti…