b'Hongyuan Zha'

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...

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...

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...

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....

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...

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...

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...

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...

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...

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...

Zhiwei Tang, et al. ∙

research

∙ 09/15/2021

Self-Training with Differentiable Teacher

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

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...

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...

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...

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...

Zeyu Qin, et al. ∙

research

∙ 03/14/2021

Mean Field Game GAN

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

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 ...

Wei Zhang, et al. ∙

research

∙ 03/01/2021

Reinforcement Learning for Adaptive Mesh Refinement

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

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...

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...

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...

Shu Liu, et al. ∙

research

∙ 02/04/2021

Hawkes Processes on Graphons

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

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...

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 ...

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...

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...

Jie Wang, et al. ∙

research

∙ 07/09/2020

Inductive Relational Matrix Completion

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

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...

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...

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...

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...

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...

Ruilin Li, et al. ∙

research

∙ 06/10/2020

Learning to Incentivize Other Learning Agents

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

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...

Wenhao Li, et al. ∙

research

∙ 02/22/2020

Learning Cost Functions for Optimal Transport

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

Haodong Sun, et al. ∙

research

∙ 02/21/2020

Transformer Hawkes Process

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

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...

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...

Wen Wang, et al. ∙

research

∙ 02/16/2020

Differentiable Top-k Operator with Optimal Transport

The top-k operation, i.e., finding the k largest or smallest elements fr...

Yujia Xie, et al. ∙

research

∙ 02/11/2020

Hyper-Meta Reinforcement Learning with Sparse Reward

Despite their success, existing meta reinforcement learning methods stil...

Yun Hua, et al. ∙

research

∙ 02/11/2020

Learning Structured Communication for Multi-agent Reinforcement Learning

This work explores the large-scale multi-agent communication mechanism u...

Junjie Sheng, et al. ∙

research

∙ 02/10/2020

Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation

Generative Adversarial Networks (GANs) have achieved great success in un...

Minshuo Chen, et al. ∙

research

∙ 02/10/2020

Learning Stochastic Behaviour of Aggregate Data

Learning nonlinear dynamics of aggregate data is a challenging problem s...

Shaojun Ma, et al. ∙

research

∙ 02/01/2020

Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Domain-adapted sentiment classification refers to training on a labeled ...

Qianming Xue, et al. ∙

research

∙ 12/07/2019

Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

Human players in professional team sports achieve high level coordinatio...

Jiachen Yang, et al. ∙

research

∙ 11/20/2019

Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

Generalized zero-shot learning (GZSL) tackles the problem of learning to...

Junjie Wang, et al. ∙

research

∙ 10/17/2019

Single Episode Policy Transfer in Reinforcement Learning

Transfer and adaptation to new unknown environmental dynamics is a key c...

Jiachen Yang, et al. ∙

research

∙ 10/10/2019

Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

We consider off-policy policy evaluation when the trajectory data are ge...

Xinyun Chen, et al. ∙

research

∙ 10/04/2019

Learning Robust Representations with Graph Denoising Policy Network

Graph representation learning, aiming to learn low-dimensional represent...

Lu Wang, et al. ∙

research

∙ 09/28/2019

Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models

Deep generative models are generally categorized into explicit models an...

Qitian Wu, et al. ∙

Hongyuan Zha

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