The Greedy Advantage in Finite-Horizon Bandits
Organizations increasingly rely on sequential experimentation to improve decision-making. While the multi-armed bandit literature has developed algorithms with strong asymptotic regret guarantees, many practical applications operate over finite and externally imposed horizons. Motivated by the finite-horizon setting, we develop a class of regularized greedy algorithms for multi-armed Bernoulli bandits. We derive the first finite-horizon regret envelopes for regularized greedy bandits, showing that finite-horizon regret decomposes into transient exploration costs and a suboptimal convergence term that decays exponentially with the regularization strength. This characterization yields princ...
| Authors | Kai Zhou, Michael Lingzhi Li, Kai Wang |
|---|---|
| Primary category | stat.ML |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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