Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
Author:
Affiliation:
1. Amazon
2. Deepmind
3. Goizueta Business School, Emory University
4. Daniels School of Business, Purdue University
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2311364
Reference51 articles.
1. Abbasi-Yadkori Y. Pál D. and Szepesvári C. (2011) “Improved algorithms for linear stochastic bandits ” Advances in Neural Information Processing Systems 24 2312–2320.
2. Ahn D. Kim S. and Kang U. (2021) “Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values ” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management pp. 2822–2826.
3. Allen G. (2012) “Sparse Higher-Order Principal Components Analysis ” Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics 22 27–36.
4. Audibert J.-Y. Munos R. and Szepesvári C. (2009) “Exploration–exploitation tradeoff using variance estimates in multi-armed bandits ” Theoretical Computer Science 410 1876–1902.
5. Auer P. (2002) “Using Confidence Bounds for Exploitation-Exploration Trade-offs ” Journal of Machine Learning Research 3 397–422.
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