2 citations · 3 across the 7 of their papers we have counts for
6 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…
Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Max Horwitz, Jake Gonzales, Eric Mazumdar +1
A growing line of work reframes preference-based fine-tuning of large language models game-theoretically: Nash Learning from Human Feedback (NLHF) recasts the problem as a zero-sum…
Convergent Q-Learning for Infinite-Horizon General-Sum Markov Games through Behavioral Economics
Yizhou Zhang, Eric Mazumdar
Risk-aversion and bounded rationality are two key characteristics of human decision-making. Risk-averse quantal-response equilibrium (RQE) is a solution concept that incorporates t…
Tractable Equilibrium Computation in Markov Games through Risk Aversion
Eric Mazumdar, Kishan Panaganti, Laixi Shi
A significant roadblock to the development of principled multi-agent reinforcement learning is the fact that desired solution concepts like Nash equilibria may be intractable to co…
Understanding Model Selection For Learning In Strategic Environments
Tinashe Handina, Eric Mazumdar
The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over$\unicode{x2013}$and the more data one…