Neural Dueling Bandits
<|reference_start|>Neural Dueling Bandits: Contextual dueling bandit is used to model the bandit problems, where a learner's goal is to find the best arm for a given context using observed noisy preference feedback over the selected arms for the past contexts. However, existing algorithms assume the reward function is linear, which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. We propose upper confidence bound- and Thompson sampling-based algorithms with sub-linear regret guarantees that efficiently select arms in each round. We then extend our theoretical results to contextual bandit problems with binary feedback, which is in itself a non-trivial contribution. Experimental results on the problem instances derived from synthetic datasets corroborate our theoretical results.<|reference_end|>
@article{verma2024neural,
    title={Neural Dueling Bandits},
    author={Arun Verma, Zhongxiang Dai, Xiaoqiang Lin, Patrick Jaillet, Bryan Kian
  Hsiang Low},
    journal={arXiv preprint arXiv:2407.17112},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17112},
    primaryClass={cs.LG cs.AI stat.ML}
}