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New research offers faster rates for offline contextual bandits

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv introduces a streamlined analysis for forward-KL-regularized offline contextual bandits. The study provides the first upper bounds demonstrating an O(epsilon^{-1}) sample complexity in both tabular and general function approximation settings, under single-policy concentrability conditions. This work addresses a gap in existing analyses, which previously resulted in slower O(epsilon^{-2}) rates for forward-KL objectives, and includes rate-optimal lower bounds to confirm the tightness of the new upper bounds. AI

IMPACT This research could lead to more efficient algorithms for decision-making in scenarios with limited data, potentially improving applications in areas like personalized recommendations or resource allocation.

RANK_REASON Academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New research offers faster rates for offline contextual bandits

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Academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Quanquan Gu ·

    Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

    arXiv:2605.09214v2 Announce Type: replace-cross Abstract: \emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have demonstrated $\epsilon^{-1}$-type fast rates for decisi…

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