mlsanityData to 7 Oct 2026

The Elliptical Potential Lemma Revisited

arXiv 2010.10182 ↗First posted 20 Oct 2020stat.ML cs.LGcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 8; 2024: 1; 2025: 3; 2026: 5.

23 papers.

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  1. 2021Model-free Representation Learning and Exploration in Low-rank MDPscited by 82
  2. 2023Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiencycited by 39
  3. 2022Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimalitycited by 24
  4. 2021Privacy Amplification via Shuffling for Linear Contextual Banditscited by 17
  5. 2021Bandit Algorithms for Precision Medicinecited by 12
  6. 2023Federated Linear Contextual Bandits with User-level Differential Privacycited by 12
  7. 2024Improved Algorithms for Contextual Dynamic Pricingcited by 11
  8. 2023Likelihood Ratio Confidence Sets for Sequential Decision Makingcited by 10
  9. 2023Sample Complexity of Kernel-Based Q-Learningcited by 8
  10. 2021Anti-Concentrated Confidence Bonuses for Scalable Explorationcited by 7
  11. 2023Provably Efficient UCB-type Algorithms For Learning Predictive State Representationscited by 6
  12. 2025Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysiscited by 3
11 more
  1. 2025No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processescited by 3
  2. 2023Effective Dimension in Bandit Problems under Censorshipcited by 2
  3. 2026f-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analysescited by 2
  4. 2021The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Samplingcited by 1
  5. 2023Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contextscited by 1
  6. 2026Prior Diffusiveness and Regret in the Linear-Gaussian Banditcited by 1
  7. 2026Exploration and Online Transfer with Behavioral Foundation Modelscited by 1
  8. 2023Posterior Sampling-based Online Learning for Episodic POMDPscited by 0
  9. 2025Constrained Linear Thompson Samplingcited by 0
  10. 2026A Direct Approach for Handling Contextual Bandits with Latent State Dynamicscited by 0
  11. 2026Adaptive Bandit Algorithms for Contextual Matching Marketscited by 0