Artificial intelligence (AI) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …

Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm
for aligning large language models (LLMs) with human preferences. Typically, RLHF …

A hallmark of human intelligence is the ability to understand and influence other minds.
Humans engage in inferential social learning (ISL) by using commonsense psychology to …

Research in Fairness, Accountability, Transparency, and Ethics (FATE) 1 has established
many sources and forms of algorithmic harm, in domains as diverse as health care, finance …

Games have a long history as benchmarks for progress in artificial intelligence. Approaches
using search and learning produced strong performance across many perfect information …

Recent breakthroughs in large language models (LLMs) have brought remarkable success
in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is that the information …

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 …

Deceptive agents are a challenge for the safety, trustworthiness, and cooperation of AI
systems. We focus on the problem that agents might deceive in order to achieve their goals …

No-regret learners seek to minimize the difference between the loss they cumulated through
the actions they played, and the loss they would have cumulated in hindsight had they …

We consider the problem of decentralized multi-agent reinforcement learning in Markov
games. A fundamental question is whether there exist algorithms that, when run …