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

2026-02-19

AI Summary

This work addresses the problem of agents actively refining their own features during online learning to obtain more favorable labels. It introduces an enhanced agent learning framework that, for the first time, supports multiclass classification, incorporates arm feedback, and respects budget constraints. By integrating feature refinement costs and a budget mechanism, the paper formulates an online learning model that embeds strategic agent behavior as a game-theoretic component and defines a novel combinatorial dimension to characterize learnability within this setting. Theoretical analysis establishes necessary and sufficient conditions for learnability under this paradigm, thereby extending the boundaries of online learning theory and providing a rigorous foundation for real-world systems that must balance agent incentives with learner performance.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Multiagent Learning
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• online learning
• improving agents
• multiclass classification
• bandit feedback
• strategic agents

Innovation

• online learning
• learning with improvements
• multiclass classification
• bandit feedback
• budgeted agents