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Found 3 papers, 1 papers with code
Learning to Defer in Content Moderation: The Human-AI Interplay
The classical learning-theoretic way to capture this human-AI interplay is via the framework of learning to defer, where the algorithm has the option to defer a classification task to humans for a fixed cost and immediately receive feedback.
Efficient decentralized multi-agent learning in asymmetric bipartite queueing systems
We study decentralized multi-agent learning in bipartite queueing systems, a standard model for service systems.
The Mean-Squared Error of Double Q-Learning
In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning.
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