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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.01250 |
| Etiquetas: |
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- In this paper, we address the contextual dueling bandit problem by proposing variance-aware algorithms that leverage neural networks to approximate nonlinear utility functions. Our approach employs a \textit{variance-aware exploration strategy}, which adaptively accounts for uncertainty in pairwise comparisons while relying only on the gradients with respect to the learnable parameters of the last layer. This design effectively balances the exploration--exploitation tradeoff under both the Upper Confidence Bound (UCB) and Thompson Sampling (TS) frameworks. As a result, under standard assumptions, we establish theoretical guarantees showing that our algorithms achieve sublinear cumulative average regret of order $\bigol\lt(d \sqrt{\sum_{t=1}^T σ_t^2} + \sqrt{dT}\rt),$ for sufficiently wide neural networks, where $ d $ is the contextual dimension, $ σ_t^2 $ the variance of comparisons at round $ t $, and $ T $ the total number of rounds. We also empirically validate that our approach offers reasonable computational efficiency and achieves sublinear regret on both synthetic tasks with nonlinear utilities and real-world tasks, outperforming existing methods.