b'Hongyuan Zha'

research

∙ 07/20/2023

Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples

Backdoor attacks are serious security threats to machine learning models...

0 Shaokui Wei, et al. ∙

research

∙ 06/29/2023

Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features

Recent studies have demonstrated the susceptibility of deep neural netwo...

0 Mingli Zhu, et al. ∙

research

∙ 06/08/2023

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

Over-generalization is a thorny issue in cognitive science, where people...

0 Junjie Sheng, et al. ∙

research

∙ 05/18/2023

Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning

The difficulty of appropriately assigning credit is particularly heighte...

0 Wenhao Li, et al. ∙

research

∙ 05/10/2023

Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas

In multi-agent reinforcement learning, each agent acts to maximize its i...

0 Yun Hua, et al. ∙

research

∙ 05/08/2023

Information Design in Multi-Agent Reinforcement Learning

Reinforcement learning (RL) mimics how humans and animals interact with ...

0 Yue Lin, et al. ∙

research

∙ 02/23/2023

Diverse Policy Optimization for Structured Action Space

Enhancing the diversity of policies is beneficial for robustness, explor...

0 Wenhao Li, et al. ∙

research

∙ 01/31/2023

Learning Roles with Emergent Social Value Orientations

Social dilemmas can be considered situations where individual rationalit...

0 Wenhao Li, et al. ∙

research

∙ 12/30/2022

Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport

We study the problem of estimating latent population flows from aggregat...

0 Sikun Yang, et al. ∙

research

∙ 10/31/2022

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

The permutation flow shop scheduling (PFSS), aiming at finding the optim...

0 Longkang Li, et al. ∙

research

∙ 10/09/2022

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Few-shot classification aims to learn a classifier to recognize unseen c...

0 Dandan Guo, et al. ∙

research

∙ 09/15/2022

Differentially Private Estimation of Hawkes Process

Point process models are of great importance in real world applications....

0 Simiao Zuo, et al. ∙

research

∙ 08/05/2022

Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification

Imbalanced data pose challenges for deep learning based classification m...

0 Dandan Guo, et al. ∙

research

∙ 06/25/2022

BackdoorBench: A Comprehensive Benchmark of Backdoor Learning

Backdoor learning is an emerging and important topic of studying the vul...

0 Baoyuan Wu, et al. ∙

research

∙ 02/28/2022

Learning Neural Hamiltonian Dynamics: A Methodological Overview

The past few years have witnessed an increased interest in learning Hami...

0 Zhijie Chen, et al. ∙

research

∙ 02/09/2022

Obtaining Dyadic Fairness by Optimal Transport

Fairness has been taken as a critical metric on machine learning models....

6 Moyi Yang, et al. ∙

research

∙ 02/08/2022

Multi-Agent Path Finding with Prioritized Communication Learning

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale...

0 Wenhao Li, et al. ∙

research

∙ 12/20/2021

Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning

Critical sectors of human society are progressing toward the adoption of...

0 Jiachen Yang, et al. ∙

research

∙ 12/09/2021

VMAgent: Scheduling Simulator for Reinforcement Learning

A novel simulator called VMAgent is introduced to help RL researchers be...

0 Junjie Sheng, et al. ∙

research

∙ 10/18/2021

Learning Prototype-oriented Set Representations for Meta-Learning

Learning from set-structured data is a fundamental problem that has rece...

0 Dandan Guo, et al. ∙

research

∙ 10/18/2021

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient

Improving the resilience of a network protects the system from natural d...

0 Shanchao Yang, et al. ∙

research

∙ 10/15/2021

Low-rank Matrix Recovery With Unknown Correspondence

We study a matrix recovery problem with unknown correspondence: given th...

0 Zhiwei Tang, et al. ∙

research

∙ 09/15/2021

Self-Training with Differentiable Teacher

Self-training achieves enormous success in various semi-supervised and w...

0 Simiao Zuo, et al. ∙

research

∙ 09/08/2021

Sqrt(d) Dimension Dependence of Langevin Monte Carlo

This article considers the popular MCMC method of unadjusted Langevin Mo...

0 Ruilin Li, et al. ∙

research

∙ 06/03/2021

Influence Estimation and Maximization via Neural Mean-Field Dynamics

We propose a novel learning framework using neural mean-field (NMF) dyna...

0 Shushan He, et al. ∙

research

∙ 05/18/2021

Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach

Multi-agent reinforcement learning (MARL) becomes more challenging in th...

0 Yan Li, et al. ∙

research

∙ 04/23/2021

Theoretical Study of Random Noise Defense against Query-Based Black-Box Attacks

The query-based black-box attacks, which don't require any knowledge abo...

0 Zeyu Qin, et al. ∙

research

∙ 03/14/2021

Mean Field Game GAN

We propose a novel mean field games (MFGs) based GAN(generative adversar...

0 Shaojun Ma, et al. ∙

research

∙ 03/05/2021

Graph-Based Tri-Attention Network for Answer Ranking in CQA

In community-based question answering (CQA) platforms, automatic answer ...

2 Wei Zhang, et al. ∙

research

∙ 03/01/2021

Reinforcement Learning for Adaptive Mesh Refinement

Large-scale finite element simulations of complex physical systems gover...

17 Jiachen Yang, et al. ∙

research

∙ 02/21/2021

Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition

Non-stationarity is one thorny issue in multi-agent reinforcement learni...

0 Wenhao Li, et al. ∙

research

∙ 02/09/2021

Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning

When solving a complex task, humans will spontaneously form teams and to...

0 Wenhao Li, et al. ∙

research

∙ 02/05/2021

Learning High Dimensional Wasserstein Geodesics

We propose a new formulation and learning strategy for computing the Was...

19 Shu Liu, et al. ∙

research

∙ 02/04/2021

Hawkes Processes on Graphons

We propose a novel framework for modeling multiple multivariate point pr...

0 Hongteng Xu, et al. ∙

research

∙ 12/19/2020

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances

For the traveling salesman problem (TSP), the existing supervised learni...

0 Zhang-Hua Fu, et al. ∙

research

∙ 12/10/2020

Learning Graphons via Structured Gromov-Wasserstein Barycenters

We propose a novel and principled method to learn a nonparametric graph ...

0 Hongteng Xu, et al. ∙

research

∙ 11/30/2020

A Hypergradient Approach to Robust Regression without Correspondence

We consider a regression problem, where the correspondence between input...

0 Yujia Xie, et al. ∙

research

∙ 11/08/2020

Reliable Off-policy Evaluation for Reinforcement Learning

In a sequential decision-making problem, off-policy evaluation (OPE) est...

0 Jie Wang, et al. ∙

research

∙ 07/09/2020

Inductive Relational Matrix Completion

Data sparsity and cold-start issues emerge as two major bottlenecks for ...

0 Qitian Wu, et al. ∙

research

∙ 07/07/2020

GraphOpt: Learning Optimization Models of Graph Formation

Formation mechanisms are fundamental to the study of complex networks, b...

18 Rakshit Trivedi, et al. ∙

research

∙ 06/29/2020

Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification

Graph neural networks are promising architecture for learning and infere...

2 Kai Zhang, et al. ∙

research

∙ 06/16/2020

Network Diffusions via Neural Mean-Field Dynamics

We propose a novel learning framework based on neural mean-field dynamic...

0 Shushan He, et al. ∙

research

∙ 06/16/2020

Hessian-Free High-Resolution Nesterov Acceleration for Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...

0 Ruilin Li, et al. ∙

research

∙ 06/16/2020

Hessian-Free High-Resolution Nesterov Accelerationfor Sampling

We propose an accelerated-gradient-based MCMC method. It relies on a mod...

0 Ruilin Li, et al. ∙

research

∙ 06/10/2020

Learning to Incentivize Other Learning Agents

The challenge of developing powerful and general Reinforcement Learning ...

0 Jiachen Yang, et al. ∙

research

∙ 04/17/2020

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

Traditional centralized multi-agent reinforcement learning (MARL) algori...

9 Wenhao Li, et al. ∙

research

∙ 02/22/2020

Learning Cost Functions for Optimal Transport

Learning the cost function for optimal transport from observed transport...

10 Haodong Sun, et al. ∙

research

∙ 02/21/2020

Transformer Hawkes Process

Modern data acquisition routinely produce massive amounts of event seque...

5 Simiao Zuo, et al. ∙

research

∙ 02/20/2020

Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients

Common Stochastic Gradient MCMC methods approximate gradients by stochas...

3 Ruilin Li, et al. ∙

research

∙ 02/19/2020

Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

Session-based target behavior prediction aims to predict the next item t...

0 Wen Wang, et al. ∙

Hongyuan Zha

Featured Co-authors