Regret Analysis of Dyadic Search
Fran\c{c}ois Bachoc, Tommaso Cesari, Roberto Colomboni, Andrea Paudice
TL;DR
This paper provides an analysis of the cumulative regret associated with the Dyadic Search algorithm, offering insights into its performance and efficiency in search tasks.
Contribution
It offers a theoretical regret analysis of the Dyadic Search algorithm, which was previously introduced without such analysis.
Findings
Derived bounds on cumulative regret for Dyadic Search
Insights into the algorithm's efficiency and convergence
Comparison with existing search algorithms
Abstract
We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].
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Taxonomy
TopicsData Management and Algorithms · Artificial Intelligence in Games · Advanced Bandit Algorithms Research