Deterministic Apple Tasting
Zachary Chase, Idan Mehalel

TL;DR
This paper introduces the first widely applicable deterministic algorithm for binary online classification with apple tasting feedback, establishing tight bounds and a comprehensive classification of learnability in both realizable and agnostic settings.
Contribution
It provides the first deterministic learner for apple tasting feedback, confirms a conjecture on learnability equivalence, and characterizes mistake bounds across different classes and settings.
Findings
Deterministic algorithms match randomized mistake bounds in realizable case.
A trichotomy classifies classes as easy, hard, or unlearnable in the agnostic case.
Optimal mistake bounds are established for expert advice with apple tasting feedback.
Abstract
In binary () online classification with apple tasting feedback, the learner receives feedback only when predicting . Besides some degenerate learning tasks, all previously known learning algorithms for this model are randomized. Consequently, prior to this work it was unknown whether deterministic apple tasting is generally feasible. In this work, we provide the first widely-applicable deterministic apple tasting learner, and show that in the realizable case, a hypothesis class is learnable if and only if it is deterministically learnable, confirming a conjecture of [Raman, Subedi, Raman, Tewari-24]. Quantitatively, we show that every class is learnable with mistake bound (where is the Littlestone dimension of ), and that this is tight for some classes. We further…
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Taxonomy
TopicsConsumer Market Behavior and Pricing
