arXiv AI
Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
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arXiv AI
Oct 31
SHPRR: An Algorithm for Pure Exploration under Constrained Regret
The paper introduces SHPRR, an algorithm that adapts Sequential Halving for pure exploration problems where regret relative to a baseline is a key cost metric. It shows that regret cost decreases as the algorithm progresses, allowing a front‑loaded allocation of the regret budget to improve best‑arm identification. Experiments on synthetic and real data demonstrate that SHPRR outperforms uniform regret allocation and existing baselines.
arXiv Machine Learning
Jul 21
Information-Based Exploration via Random Features for Reinforcement Learning
arXiv:2607. 17981v1 Announce Type: new Abstract: Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish.
arXiv Machine Learning
Jun 16
Best Arm Identification with Minimal Regret
arXiv:2409. 18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret.
arXiv Machine Learning
Jun 9
Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
arXiv:2606. 09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time.
arXiv Machine Learning
Jun 2
Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits
arXiv:2606. 01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret.
arXiv Machine Learning
Jun 19
Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
arXiv:2606. 20107v1 Announce Type: new Abstract: Optimal Reinforcement Learning (RL) algorithms typically rely on carefully constructed count-based uncertainty estimates to drive exploration.