15 citations · 59 across the 17 of their papers we have counts for
19 papers
Identity, Incentives, and Cooperative Behavior for AI: Reverse-Engineering Multiagent Cooperation
Andrew Perrault, Bryan Wilder
The behavior of AI systems increasingly depends on the extent to which agents cooperate or coordinate with one another, in ways that may be desirable or not depending on the situat…
Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design
Amin Meghrazi, Srinivasan Parthasarathy, Andrew Perrault
Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in model efficacy. This paper ex…
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…
Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
Sanket Shah, Andrew Perrault, Bryan Wilder +1
Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a…
Normality-Guided Distributional Reinforcement Learning for Continuous Control
Ju-Seung Byun, Andrew Perrault
Learning a predictive model of the mean return, or value function, plays a critical role in many reinforcement learning algorithms. Distributional reinforcement learning (DRL) has…
Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses
Sanket Shah, Kai Wang, Bryan Wilder +2
Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specif…