14 citations · 31 across the 17 of their papers we have counts for
11 papers · 1 filter
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…