New regularized greedy algorithms improve finite-horizon bandit performance

Researchers have developed a new class of regularized greedy algorithms designed for multi-armed Bernoulli bandits operating within finite horizons. These algorithms offer the first derived finite-horizon regret envelopes for such policies, demonstrating that regret can be broken down into exploration costs and a convergence term that diminishes exponentially with increased regularization. This analysis provides a method for calibrating regularization parameters, leading to improved regret guarantees for the standard greedy policy and outperforming existing state-of-the-art algorithms in numerical experiments. AI

IMPACT Introduces improved algorithms for decision-making in finite-horizon experimentation settings.

RANK_REASON The cluster contains a research paper detailing new algorithms for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Bernoulli bandits

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →