Neural Logistic Bandits

Seoungbin Bae; Dabeen Lee

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arXiv · 2505.02069

Neural Logistic Bandits

Seoungbin Bae·Dabeen Lee

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Abstract

We study the problem of neural logistic bandits, where the main task is to learn an unknown reward function within a logistic link function using a neural network. Existing approaches either exhibit unfavorable dependencies on $κ$, where $1/κ$ represents the minimum variance of reward distributions, or suffer from direct dependence on the feature dimension $d$, which can be huge in neural network-based settings. In this work, we introduce a novel Bernstein-type inequality for self-normalized vector-valued martingales that is designed to bypass a direct dependence on the ambient dimension. This lets us deduce a regret upper bound that grows with the effective dimension $\widetilde{d}$, not the feature dimension, while keeping a minimal dependence on $κ$. Based on the concentration inequality, we propose two algorithms, NeuralLog-UCB-1 and NeuralLog-UCB-2, that guarantee regret upper bounds of order $\widetilde{O}(\widetilde{d}\sqrt{κT})$ and $\widetilde{O}(\widetilde{d}\sqrt{T/κ})$, respectively, improving on the existing results. Lastly, we report numerical results on both synthetic and real datasets to validate our theoretical findings.

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BibTeX RIS

Seoungbin Bae, Dabeen Lee. 2026-05-28. Neural Logistic Bandits. https://arxiv.org/abs/2505.02069

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