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arXiv · 2603.16755

A Practical Algorithm for Feature-Rich, Non-Stationary Bandit Problems

Wei Min Loh·Sajib Kumer Sinha·Ankur Agarwal·Pascal Poupart
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Abstract

Contextual bandits are incredibly useful in many practical problems. We go one step further by devising a more realistic problem that combines: (1) contextual bandits with dense arm features, (2) non-linear reward functions, and (3) a generalization of correlated bandits where reward distributions change over time but the degree of correlation maintains. This formulation lends itself to a wider set of applications such as recommendation tasks. To solve this problem, we introduce conditionally coupled contextual C3 Thompson sampling for Bernoulli bandits. It combines an improved Nadaraya-Watson estimator on an embedding space with Thompson sampling that allows online learning without retraining. Empirical results show that C3 outperforms the next best algorithm by 5.7% lower average cumulative regret on four OpenML tabular datasets as well as demonstrating a 12.4% click lift on Microsoft News Dataset (MIND) compared to other algorithms.

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Wei Min Loh, Sajib Kumer Sinha, Ankur Agarwal, Pascal Poupart. 2026-03-17. A Practical Algorithm for Feature-Rich, Non-Stationary Bandit Problems. https://arxiv.org/abs/2603.16755

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