LGOCSep 2, 2022

Regret Analysis of Dyadic Search

arXiv:2209.00885v22 citationsh-index: 12
Originality Synthesis-oriented
AI Analysis

This work offers incremental theoretical insights for researchers in optimization or bandit algorithms.

The paper analyzes the cumulative regret of the Dyadic Search algorithm, providing theoretical bounds on its performance.

We analyze the cumulative regret of the Dyadic Search algorithm of Bachoc et al. [2022].

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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