# Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features

arXiv: https://arxiv.org/abs/2409.09199 · HTML: https://mlsanity.com/paper/2409.09199/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Doubly-Robust Lasso Bandit](https://mlsanity.com/paper/1907.11362/) (2019)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits](https://mlsanity.com/paper/2008.11918/) (2020)
- [Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits](https://mlsanity.com/paper/2110.00158/) (2021)

## Abstract (arXiv)

Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sparsity and batched data. We address the challenge of fairness by excluding irrelevant features from decision-making processes using a novel algorithm, Online Batched Sequential Inclusion (OBSI), which sequentially includes features as confidence in their impact on the reward increases. Our experiments on synthetic data show the superior performance of OBSI compared to other algorithms in terms of regret, relevance of features used, and compute.