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🏢 University of Iowa

Provably Efficient Interactive-Grounded Learning with Personalized Reward
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AI Generated Machine Learning Reinforcement Learning 🏢 University of Iowa
Provably efficient algorithms are introduced for interaction-grounded learning (IGL) with context-dependent feedback, addressing the lack of theoretical guarantees in existing approaches for personali…
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
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AI Theory Fairness 🏢 University of Iowa
This paper solves the open problem of achieving no-regret learning in online multi-agent Nash social welfare maximization.
Contextual Multinomial Logit Bandits with General Value Functions
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Machine Learning Reinforcement Learning 🏢 University of Iowa
Contextual MNL bandits are revolutionized with general value functions, offering enhanced algorithms for stochastic and adversarial settings, surpassing previous results in accuracy and efficiency.