Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors
arXiv:2602.03972v3 Announce Type: replace-cross Abstract: The best-arm identification (BAI) problem is one of the most fundamental problems in interactive machine learning, which has two flavors: the fixed-budget setting (FB) and the fixed-confidence setting (FC). For $K$-armed bandits with a unique best arm, the optimal sample complexities for both settings have been settled down, and they match up to logarithmic factors. This prompts an interesting research question about the generic, potentially structured BAI problems: is FB harder than FC or the other way around? In this paper, we show th
This paper refines theoretical understanding in interactive machine learning, a foundational area for AI agent development, at a time when practical applications of AI agents are rapidly emerging.
Improved theoretical understanding of best-arm identification can lead to more efficient and robust AI systems, reducing computational costs and accelerating AI development, especially for decision-making agents.
The clarified relationship between fixed-budget and fixed-confidence settings offers insights that may streamline the design and optimization of advanced sequential decision-making algorithms within AI.
- · AI algorithm developers
- · Machine learning researchers
- · Autonomous system designers
More efficient resource allocation in reinforcement learning and bandit problems.
Accelerated development of AI agents capable of optimal learning with limited resources.
Reduced 'trial and error' phases in AI deployment, potentially lowering barriers to entry for AI applications in various industries.
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