4 citations · 17 across the 34 of their papers we have counts for
7 papers · 1 filter
Strategic Decision Focused Learning
Tinashe Handina, Yuehan Diao, Adam Wierman +1
Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized…
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina, Tongxin Li, Kishan Panaganti +2
We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice lands…
Small Bottle, Big Pipe: Quantifying and Addressing the Impact of Data Centers on Public Water Systems
Yuelin Han, Pengfei Li, Adam Wierman +1
Water is a critical resource for data centers and an efficient means of cooling. However, meeting the growing water demand of data centers requires substantial peak water withdrawa…
Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
Emile Anand, Richard Hoffmann, Sarah Liaw +1
Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales expo…
Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization
Chengrui Qu, Christopher Yeh, Kishan Panaganti +2
Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution, where value-factorization methods enforce the individual-gl…
Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity
Yingxuan Yang, Chengrui Qu, Muning Wen +5
LLM-based multi-agent systems (MAS) have emerged as a promising approach to tackle complex tasks that are difficult for individual LLMs. A natural strategy is to scale performance…
SCaLE: Switching Cost aware Learning and Exploration
Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman
This work addresses the fundamental problem of unbounded metric movement costs in bandit online convex optimization, by considering high-dimensional dynamic quadratic hitting costs…