For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Despite much progress in training artificial intelligence (AI) systems to imitate human
language, building agents that use language to communicate intentionally with humans in …

One of the fundamental quests of AI is to produce agents that coordinate well with humans.
This problem is challenging, especially in domains that lack high quality human behavioral …

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 …

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 …

If machine learning models were to achieve superhuman abilities at various reasoning or
decision-making tasks, how would we go about evaluating such models, given that humans …

Large-scale data is crucial for learning realistic and capable driving policies. However, it can
be impractical to rely on scaling datasets with real data alone. The majority of driving data is …

Robustly cooperating with unseen agents and human partners presents significant
challenges due to the diverse cooperative conventions these partners may adopt. Existing …

When applied to question answering and other text generation tasks, language models
(LMs) may be queried generatively (by sampling answers from their output distribution) or …