arXiv:2605.09200v2 Announce Type: replace Abstract: We study adversarial noisy bandits given a known function class $\mathcal{F}$. In each round, the adversary selects a function $f \in \mathcal{F}$, the learner chooses an arm, and then observes a noisy reward determined by the chosen arm and the function $f$. The goal is to minimize the cumulative regret $R(T)$, defined as the difference between the learner's performance and that of the best fixed arm in hindsight over $T$ rounds. We say that a function class $\mathcal{F}$ is learnable if there exists an algorithm achieving sublinear regret.
This research provides a complete characterization of learnability in adversarial noisy bandits, which is a fundamental challenge in theoretical machine learning, building on previous work in the field.
Understanding the limits and capabilities of learning algorithms in adversarial environments is crucial for developing robust and reliable AI systems, impacting their real-world deployment.
This paper offers a theoretical foundation for designing algorithms that can perform optimally even when faced with uncertainty and adversarial interference, which can lead to more predictable AI behavior.
- · AI researchers
- · Machine learning theoreticians
- · Developers of robust AI systems
- · Malicious actors exploiting AI vulnerabilities
- · Systems unprepared for adversarial conditions
Improved theoretical understanding of AI learnability in challenging environments.
Development of more resilient and trustworthy AI algorithms for practical applications.
Enhanced trust in AI systems deployed in critical, uncertain, or adversarial domains.
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Read at arXiv cs.LG