# An aggregating strategy for shifting experts in discrete sequence prediction

arXiv: https://arxiv.org/abs/1708.01744 · HTML: https://mlsanity.com/paper/1708.01744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [The robust way to stack and bag: the local Lipschitz way](https://mlsanity.com/paper/2206.00513/) (2022)

## Abstract (arXiv)

We study how we can adapt a predictor to a non-stationary environment with advises from multiple experts. We study the problem under complete feedback when the best expert changes over time from a decision theoretic point of view. Proposed algorithm is based on popular exponential weighing method with exponential discounting. We provide theoretical results bounding regret under the exponential discounting setting. Upper bound on regret is derived for finite time horizon problem. Numerical verification of different real life datasets are provided to show the utility of proposed algorithm.