Modifying Squint for Prediction with Expert Advice in a Changing Environment
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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.
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6 of 15 references.
References · 6
- 2016Coin Betting and Parameter-Free Online Learningcited by 131
- 2015Strongly Adaptive Online Learningcited by 102
- 2016Improved Strongly Adaptive Online Learning using Coin Bettingcited by 48
- 2015Second-order Quantile Methods for Experts and Combinatorial Gamescited by 47
- 2018The Many Faces of Exponential Weights in Online Learningcited by 33
- 2016Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learningcited by 13
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