# Hyper-parameter Tuning for the Contextual Bandit

arXiv: https://arxiv.org/abs/2005.02209 · HTML: https://mlsanity.com/paper/2005.02209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 3 of 3)

- [Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms](https://mlsanity.com/paper/2106.02979/) (2021)
- [Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits](https://mlsanity.com/paper/2302.09440/) (2023)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)

## Abstract (arXiv)

We study here the problem of learning the exploration exploitation trade-off in the contextual bandit problem with linear reward function setting. In the traditional algorithms that solve the contextual bandit problem, the exploration is a parameter that is tuned by the user. However, our proposed algorithm learn to choose the right exploration parameters in an online manner based on the observed context, and the immediate reward received for the chosen action. We have presented here two algorithms that uses a bandit to find the optimal exploration of the contextual bandit algorithm, which we hope is the first step toward the automation of the multi-armed bandit algorithm.