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arXiv · 2602.17103

Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners

Sajad Ashkezari·Shai Ben-David
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Abstract

We investigate the recently introduced model of learning with improvements, where agents are allowed to make small changes to their feature values to be warranted a more desirable label. We extensively extend previously published results by providing combinatorial dimensions that characterize online learnability in this model, by analyzing the multiclass setup, learnability in a bandit feedback setup, modeling agents' cost for making improvements and more.

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Sajad Ashkezari, Shai Ben-David. 2026-02-19. Online Learning with Improving Agents: Multiclass, Budgeted Agents and Bandit Learners. https://arxiv.org/abs/2602.17103

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