Given a set of discrete probability distributions, the minimum entropy coupling is the
minimum entropy joint distribution that has the input distributions as its marginals. This has …

The rapid proliferation of frontier model agents promises significant societal advances but
also raises concerns about systemic risks arising from unsafe interactions. Collusion to the …

Explicit communication among humans is key to coordinating and learning. Social learning,
which uses cues from experts, can greatly benefit from the usage of explicit communication …

By enabling agents to communicate, recent cooperative multi-agent reinforcement learning
(MARL) methods have demonstrated better task performance and more coordinated …

The impact of communication on decision-making systems has been extensively studied
under the assumption of dedicated communication channels. We instead consider …

Minimum-entropy coupling (MEC)--the process of finding a joint distribution with minimum
entropy for given marginals--has applications in areas such as causality and steganography …

This thesis explores three critical dimensions in machine learning: modeling, training, and
theory. Each dimension, represented by studies in brain imaging, distributed computing, and …

Explicit communication among humans is key to coordinating and learning. In multi-agent
reinforcement learning for partially-observable environments, agents may convey …

A growing number of real-world control problems require teams of software agents to solve a
joint task through cooperation. Such tasks naturally arise whenever human workers are …