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Creators/Authors contains: "Lin, Jiabin"

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  1. ; ; (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  2. ; ; (, Proceedings of the 41st International Conference on Machine Learning (ICML) 2024)
    We study how representation learning can im- prove the learning efficiency of contextual bandit problems. We study the setting where we play T contextual linear bandits with dimension d si- multaneously, and these T bandit tasks collec- tively share a common linear representation with a dimensionality of r ≪ d. We present a new algorithm based on alternating projected gradi- ent descent (GD) and minimization estimator to recover a low-rank feature matrix. Using the pro- posed 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 
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    Accepted Manuscript Full Text Available
  3. ; (, IEEE Control Systems Letters)
    Full Text Available