arXiv Machine Learning June 9, 2026 By Seoungbin Bae, Dabeen Lee

Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret

Read the original on arXiv Machine Learning →

arXiv:2606. 09668v1 Announce Type: new Abstract: Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates.

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Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness

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Regret Analysis of Retry-Based Bandits

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