Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
<|reference_start|>Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits: We study how representation learning can improve the learning efficiency of contextual bandit problems. We study the setting where we play T contextual linear bandits with dimension d simultaneously, and these T bandit tasks collectively share a common linear representation with a dimensionality of r much smaller than d. We present a new algorithm based on alternating projected gradient descent (GD) and minimization estimator to recover a low-rank feature matrix. Using the proposed estimator, we present a multi-task learning algorithm for linear contextual bandits and prove the regret bound of our algorithm. We presented experiments and compared the performance of our algorithm against benchmark algorithms.<|reference_end|>
@article{lin2024fast,
    title={Fast and Sample Efficient Multi-Task Representation Learning in
  Stochastic Contextual Bandits},
    author={Jiabin Lin, Shana Moothedath, Namrata Vaswani},
    journal={arXiv preprint arXiv:2410.02068},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02068},
    primaryClass={cs.LG stat.ML}
}