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The Elliptical Potential Lemma Revisited

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arxiv 2010.10182 v1 pith:5ICGHQJO submitted 2020-10-20 stat.ML cs.LG

classification stat.MLcs.LG
keywords ellipticallemmapotentialproofresultbanditsbelieveconsidered
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This note proposes a new proof and new perspectives on the so-called Elliptical Potential Lemma. This result is important in online learning, especially for linear stochastic bandits. The original proof of the result, however short and elegant, does not give much flexibility on the type of potentials considered and we believe that this new interpretation can be of interest for future research in this field.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Prior Diffusiveness and Regret in the Linear-Gaussian Bandit

    cs.LG 2026-01 accept novelty 7.0 of 10

    Thompson sampling's Bayesian regret in linear-Gaussian bandits is Õ(σd√T + dr√trΣ0): the prior-diffusiveness burn-in is additive, not multiplicative.

  2. Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis

    cs.LG 2025-05 conditional novelty 7.0 of 10

    A unified framework proves that augmenting any confidence-based online RL algorithm with offline data yields order-optimal suboptimality-gap and regret bounds, with a new concentrability coefficient that separates the...

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