# Modifying Squint for Prediction with Expert Advice in a Changing Environment

arXiv: https://arxiv.org/abs/2209.06826 · HTML: https://mlsanity.com/paper/2209.06826/

## Key facts

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

## Builds on (most cited first, 6 of 6)

- [Coin Betting and Parameter-Free Online Learning](https://mlsanity.com/paper/1602.04128/) (2016)
- [Strongly Adaptive Online Learning](https://mlsanity.com/paper/1502.07073/) (2015)
- [Improved Strongly Adaptive Online Learning using Coin Betting](https://mlsanity.com/paper/1610.04578/) (2016)
- [Second-order Quantile Methods for Experts and Combinatorial Games](https://mlsanity.com/paper/1502.08009/) (2015)
- [The Many Faces of Exponential Weights in Online Learning](https://mlsanity.com/paper/1802.07543/) (2018)
- [Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning](https://mlsanity.com/paper/1605.06439/) (2016)

## 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.