arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing baseline safety against a fixed comparator. While existing methods excel in one or two of these regimes, they typically fail to unify all three without sacrificing optimal rates or requiring oracle access to problem-dependent parameters. In this work, we bridge this gap by introducing COMPASS-Hedge. To the best of our knowledge, our algorithm is the first full-information anytime method to simulta
The continuous evolution of AI research seeks to address fundamental limitations in online learning algorithms, particularly the trade-off between robustness, optimality, and safety.
This research addresses a core trilemma in AI, potentially leading to more reliable and safer autonomous systems that can operate effectively in uncertain real-world environments.
Algorithm COMPASS-Hedge offers a unified approach to online learning, balancing adversarial and stochastic settings while providing safety guarantees, without requiring special problem-dependent parameters.
- · AI researchers
- · AI agents developers
- · Robotics industry
- · Developers of less robust online learning algorithms
- · Systems heavily reliant on oracle access for safety
Improved performance and safety for online learning algorithms across varied applications.
Accelerated development of more reliable and adaptable AI agents and autonomous systems.
Increased public and institutional trust in AI systems due to enhanced safety and predictability.
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Read at arXiv cs.LG