# Gradient Ascent for Active Exploration in Bandit Problems

arXiv: https://arxiv.org/abs/1905.08165 · HTML: https://mlsanity.com/paper/1905.08165/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 15 of 15)

- [Information-Directed Selection for Top-Two Algorithms](https://mlsanity.com/paper/2205.12086/) (2022)
- [Cost Aware Best Arm Identification](https://mlsanity.com/paper/2402.16710/) (2024)
- [Optimal Multi-Fidelity Best-Arm Identification](https://mlsanity.com/paper/2406.03033/) (2024)
- [A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits](https://mlsanity.com/paper/2105.12978/) (2021)
- [On the complexity of All varepsilon-Best Arms Identification](https://mlsanity.com/paper/2202.06280/) (2022)
- [Best-Arm Identification in Unimodal Bandits](https://mlsanity.com/paper/2411.01898/) (2024)
- [Price of Safety in Linear Best Arm Identification](https://mlsanity.com/paper/2309.08709/) (2023)
- [Thompson Exploration with Best Challenger Rule in Best Arm Identification](https://mlsanity.com/paper/2310.00539/) (2023)
- [Best Arm Identification in Stochastic Bandits: Beyond β-optimality](https://mlsanity.com/paper/2301.03785/) (2023)
- [Constrained Pareto Set Identification with Bandit Feedback](https://mlsanity.com/paper/2506.08127/) (2025)
- [Pure Exploration with Infinite Answers](https://mlsanity.com/paper/2505.22473/) (2025)
- [Non-Asymptotic Analysis of (Sticky) Track-and-Stop](https://mlsanity.com/paper/2505.22475/) (2025)
- [Pure Exploration via Frank-Wolfe Self-Play](https://mlsanity.com/paper/2509.19901/) (2025)
- [Efficient Clustering in Stochastic Bandits](https://mlsanity.com/paper/2601.09162/) (2026)
- [Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates](https://mlsanity.com/paper/2606.21889/) (2026)

## 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.