arXiv:2512.00517v3 Announce Type: replace-cross Abstract: Sequential optimization of black-box functions from noisy evaluations has been widely studied, with Gaussian Process bandit algorithms such as GP-UCB guaranteeing no-regret in stationary settings. However, for time-varying objectives, no-regret is unattainable under pure bandit feedback unless strong and often unrealistic assumptions are imposed. We propose a novel method for optimizing time-varying rewards in the frequentist setting, where the objective has bounded RKHS norm almost surely. Time variations are captured through uncertain
The paper addresses a known limitation in current Gaussian Process optimization (GP-UCB) by proposing a method specifically for time-varying functions, indicating a push towards more dynamic and adaptive AI algorithms.
Improving optimization for time-varying objectives is crucial for real-world applications of AI, such as autonomous systems, financial modeling, and dynamic resource allocation, where environments are rarely static.
Traditional no-regret guarantees for Gaussian Process optimization were limited to stationary settings; this research expands the applicability to dynamic environments, making AI optimization more robust.
- · AI researchers
- · Robotics
- · Autonomous systems developers
- · Machine learning applications
- · Systems relying on static optimization assumptions
More robust and adaptive AI systems capable of operating effectively in dynamic, real-world conditions.
Accelerated development and deployment of autonomous agents and decision-making systems in complex, changing environments.
Enhanced efficiency and performance in areas like self-driving cars, industrial automation, and predictive maintenance in dynamic systems.
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Read at arXiv cs.LG