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cs.LG2026
A Complete Characterization of Learnability for Adversarial Noisy Bandits
Steve Hanneke, Kun Wang
We study adversarial noisy bandits given a known function class . In each round, the adversary selects a function , the learner chooses an arm, and…
cs.LG2026
Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning
Idan Attias, Steve Hanneke, Arvind Ramaswami
Agnostic online learning is classically solved via a reduction to the realizable setting, utilizing Littlestone's Standard Optimal Algorithm (SOA) as a base learner. However, the S…
cs.LG2024
Revisiting Agnostic PAC Learning
Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy
PAC learning, dating back to Valiant'84 and Vapnik and Chervonenkis'64,'74, is a classic model for studying supervised learning. In the agnostic setting, we have access to a hypoth…