mlsanityData to 14 Oct 2026

Indexed Minimum Empirical Divergence for Unimodal Bandits

arXiv 2112.01452 ↗First posted 2 Dec 2021cs.AI cs.LG math.OCcited by 4

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Abstract (arXiv)

We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm that optimally exploits the unimodal-structure, by adapting to this setting the Indexed Minimum Empirical Divergence (IMED) algorithm introduced by Honda and Takemura [2015]. Owing to our proof technique, we are able to provide a concise finite-time analysis of IMED-UB algorithm. Numerical experiments show that IMED-UB competes with the state-of-the-art algorithms.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

8 of 22 references.

References · 8

  1. 2014Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithmscited by 60
  2. 2016The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Banditscited by 49
  3. 2017Minimal Exploration in Structured Stochastic Banditscited by 41
  4. 2014Unimodal Bandits: Regret Lower Bounds and Optimal Algorithmscited by 37
  5. 2019Solving Bernoulli Rank-One Bandits with Unimodal Thompson Samplingcited by 23
  6. 2020Structure Adaptive Algorithms for Stochastic Banditscited by 19
  7. 2017Streaming kernel regression with provably adaptive mean, variance, and regularizationcited by 15
  8. 2017Boundary Crossing Probabilities for General Exponential Familiescited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Best-Arm Identification in Unimodal Banditscited by 6
  2. 2024Indexed Minimum Empirical Divergence-Based Algorithms for Linear Banditscited by 2
  3. 2025The Confusing Instance Principle for Online Linear Quadratic Controlcited by 1
  4. 2025Multimodal Bandits: Regret Lower Bounds and Optimal Algorithmscited by 1