Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback

• URL: http://arxiv.org/abs/2506.13163v1
• Date: Mon, 16 Jun 2025 07:19:02 GMT
• Title: Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
• Authors: Tanmay Goyal, Gaurav Sinha,
• Abstract summary: We study the Logistic Contextual Slate Bandit problem.<n>A single binary reward, determined by a logistic model, is observed for the chosen slate.<n>We propose two algorithms, Slate-GLM-OFU and Slate-GLM-TS, that accomplish this goal.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We study the Logistic Contextual Slate Bandit problem, where, at each round, an agent selects a slate of $N$ items from an exponentially large set (of size $2^{\Omega(N)}$) of candidate slates provided by the environment. A single binary reward, determined by a logistic model, is observed for the chosen slate. Our objective is to develop algorithms that maximize cumulative reward over $T$ rounds while maintaining low per-round computational costs. We propose two algorithms, Slate-GLM-OFU and Slate-GLM-TS, that accomplish this goal. These algorithms achieve $N^{O(1)}$ per-round time complexity via local planning (independent slot selections), and low regret through global learning (joint parameter estimation). We provide theoretical and empirical evidence supporting these claims. Under a well-studied diversity assumption, we prove that Slate-GLM-OFU incurs only $\tilde{O}(\sqrt{T})$ regret. Extensive experiments across a wide range of synthetic settings demonstrate that our algorithms consistently outperform state-of-the-art baselines, achieving both the lowest regret and the fastest runtime. Furthermore, we apply our algorithm to select in-context examples in prompts of Language Models for solving binary classification tasks such as sentiment analysis. Our approach achieves competitive test accuracy, making it a viable alternative in practical scenarios.

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