mlsanityData to 7 Oct 2026

Gradient Ascent for Active Exploration in Bandit Problems

arXiv 1905.08165 ↗First posted 20 May 2019stat.ML cs.LGcited by 15

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Abstract (arXiv)

We present a new algorithm based on an gradient ascent for a general Active Exploration bandit problem in the fixed confidence setting. This problem encompasses several well studied problems such that the Best Arm Identification or Thresholding Bandits. It consists of a new sampling rule based on an online lazy mirror ascent. We prove that this algorithm is asymptotically optimal and, most importantly, computationally efficient.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 3; 2025: 4; 2026: 2.

15 papers.

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  1. 2022Information-Directed Selection for Top-Two Algorithmscited by 15
  2. 2024Cost Aware Best Arm Identificationcited by 12
  3. 2024Optimal Multi-Fidelity Best-Arm Identificationcited by 8
  4. 2021A Non-asymptotic Approach to Best-Arm Identification for Gaussian Banditscited by 7
  5. 2022On the complexity of All varepsilon-Best Arms Identificationcited by 6
  6. 2024Best-Arm Identification in Unimodal Banditscited by 6
  7. 2023Price of Safety in Linear Best Arm Identificationcited by 4
  8. 2023Thompson Exploration with Best Challenger Rule in Best Arm Identificationcited by 4
  9. 2023Best Arm Identification in Stochastic Bandits: Beyond β-optimalitycited by 2
  10. 2025Constrained Pareto Set Identification with Bandit Feedbackcited by 2
  11. 2025Pure Exploration via Frank-Wolfe Self-Playcited by 1
  12. 2025Pure Exploration with Infinite Answerscited by 0
3 more
  1. 2025Non-Asymptotic Analysis of (Sticky) Track-and-Stopcited by 0
  2. 2026Efficient Clustering in Stochastic Banditscited by 0
  3. 2026Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimatescited by 0