The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

While real-world applications of reinforcement learning (RL) are becoming popular, the
security and robustness of RL systems are worthy of more attention and exploration. In …

We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies
against it, achieving a $> $97% win rate against KataGo running at superhuman settings …

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …

In the evolving landscape of text-to-image (T2I) diffusion models, the remarkable capability
to generate high-quality images from textual descriptions faces challenges with the potential …

Multi-Agent Reinforcement Learning (MARL) is susceptible to Adversarial Machine Learning
(AML) attacks. Execution-time AML attacks against MARL are complex due to effects that …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …