Jun Yang

papers

378

total citations

papers (10)

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

NEURIPS 2021arXiv

181

citations

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

NEURIPS 2021arXiv

101

citations

S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

AAAI 2024arXiv

citations

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency

NEURIPS 2025arXiv

citations

Conservative Offline Policy Adaptation in Multi-Agent Games

NEURIPS 2023

citations

Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

ICCV 2023

citations

Safe Opponent-Exploitation Subgame Refinement

NEURIPS 2022

citations

Jun Yang

papers (10)

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

Efficient Multi-agent Reinforcement Learning by Planning

Single-Trajectory Distributionally Robust Reinforcement Learning

Learning Diverse Risk Preferences in Population-Based Self-Play

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency

Conservative Offline Policy Adaptation in Multi-Agent Games

Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

Safe Opponent-Exploitation Subgame Refinement

papers (10)

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

Efficient Multi-agent Reinforcement Learning by Planning

Single-Trajectory Distributionally Robust Reinforcement Learning

Learning Diverse Risk Preferences in Population-Based Self-Play

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency

Conservative Offline Policy Adaptation in Multi-Agent Games

Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation

Safe Opponent-Exploitation Subgame Refinement