most citedExploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

10 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2023

Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning

Aakriti Agrawal, Rohith Aralikatti, Yanchao Sun +1

Multi-agent reinforcement learning (MARL) plays a pivotal role in tackling real-world challenges. However, the seamless transition of trained policies from simulations to real-worl…

cs.LG20231 cited

Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning

Jifeng Hu, Yanchao Sun, Sili Huang +6

Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models…

cs.LG202310 cited

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

Yuancheng Xu, Yanchao Sun, Micah Goldblum +2

The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust tr…

cs.LG20234 cited

SMART: Self-supervised Multi-task pretrAining with contRol Transformers

Yanchao Sun, Shuang Ma, Ratnesh Madaan +3

Self-supervised pretraining has been extensively studied in language and vision domains, where a unified model can be easily adapted to various downstream tasks by pretraining repr…

cs.LG20225 cited

Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh +4

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained commu…

cs.LG20222 cited

Transfer RL across Observation Feature Spaces via Model-Based Regularization

Yanchao Sun, Ruijie Zheng, Xiyao Wang +2

In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramati…