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Modifying Squint for Prediction with Expert Advice in a Changing Environment

arXiv 2209.06826 ↗First posted 14 Sep 2022cs.LGcited by 0

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Abstract (arXiv)

We provide a new method for online learning, specifically prediction with expert advice, in a changing environment. In a non-changing environment the Squint algorithm has been designed to always function at least as well as other known algorithms and in specific cases it functions much better. However, when using a conventional black-box algorithm to make Squint suitable for a changing environment, it loses its beneficial properties. Hence, we provide a new algorithm, Squint-CE, which is suitable for a changing environment and preserves the properties of Squint.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 14 Sep 2022.

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6 of 15 references.

References · 6

  1. 2016Coin Betting and Parameter-Free Online Learningcited by 131
  2. 2015Strongly Adaptive Online Learningcited by 102
  3. 2016Improved Strongly Adaptive Online Learning using Coin Bettingcited by 48
  4. 2015Second-order Quantile Methods for Experts and Combinatorial Gamescited by 47
  5. 2018The Many Faces of Exponential Weights in Online Learningcited by 33
  6. 2016Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learningcited by 13

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