14 citations · 31 across the 17 of their papers we have counts for
17 papers
Towards Optimal Randomized Strategies in Adversarial Example Game
Jiahao Xie, Chao Zhang, Weijie Liu +2
The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications. A recent line of work shows th…
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
Wensong Bai, Chao Zhang, Yichao Fu +3
In this paper, we propose the first fully push-forward-based distributional reinforcement learning algorithm, named PACER, which consists of a distributional critic, a stochastic a…
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Zebang Shen, Hui Qian, Tongzhou Mu +1
Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this p…
An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problem
Chao Zhang, Hui Qian, Jiahao Xie
Wasserstein Barycenter Problem (WBP) has recently received much attention in the field of artificial intelligence. In this paper, we focus on the decentralized setting for WBP and…
SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Graph matching finds the correspondence of nodes across two correlated graphs and lies at the core of many applications. When graph side information is not available, the node corr…
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
Chao Zhang, Zhijian Li, Hui Qian +1
The recently developed Particle-based Variational Inference (ParVI) methods drive the empirical distribution of a set of \emph{fixed-weight} particles towards a given target distri…
Approximating Optimal Transport via Low-rank and Sparse Factorization
Weijie Liu, Chao Zhang, Nenggan Zheng +1
Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck. Recently, one line of works propose to s…
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
Jiahao Xie, Chao Zhang, Zebang Shen +2
Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for m…
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
Weijie Liu, Hui Qian, Chao Zhang +3
Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artif…
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
Jiahao Xie, Zebang Shen, Chao Zhang +2
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds…
Aggregated Gradient Langevin Dynamics
Chao Zhang, Jiahao Xie, Zebang Shen +3
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic conver…
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate q…
Accelerated Variance Reduced Block Coordinate Descent
Zebang Shen, Hui Qian, Chao Zhang +1
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emp…
Riemannian Tensor Completion with Side Information
Tengfei Zhou, Hui Qian, Zebang Shen +1
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion pro…
Co-interest Person Detection from Multiple Wearable Camera Videos
Yuewei Lin, Kareem Ezzeldeen, Youjie Zhou +4
Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views. In this paper, we tackle a new problem of locating the c…
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
Hongkai Yu, Youjie Zhou, Hui Qian +6
One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeli…
A Nonconvex Approach for Structured Sparse Learning
Shubao Zhang, Hui Qian, Zhihua Zhang
Sparse learning is an important topic in many areas such as machine learning, statistical estimation, signal processing, etc. Recently, there emerges a growing interest on structur…