arXiv Machine Learning
Jul 1
A Complete Characterization of Learnability for Adversarial Noisy Bandits
arXiv:2605. 09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$.
On the Computational Tractability of Robust Bandits
The paper studies robust bandits, a framework for learning when the true environment lies outside the learner’s hypothesis class. It presents a polynomial‑time algorithm achieving ≠O(√T) regret for a specific tractable case, while showing that slight extensions of this case are NP‑hard. The work highlights the computational limits of robust bandits and suggests their relevance to AI alignment.
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arXiv Machine Learning
Jun 19
Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
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arXiv Machine Learning
Aug 13
An Efficient Near-Optimal Algorithm for Adversarial $m$-Set Bandits
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arXiv Machine Learning
Jul 9
Nonlinear Bandit
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arXiv Machine Learning
Sep 10
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arXiv Machine Learning
Aug 3
Parameter-Free Heavy-Tailed Bandits
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arXiv Machine Learning
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