Stochastic Low-Rank Tensor Bandits for Multi-Dimensional Online Decision Making
Author:
Affiliation:
1. Amazon, Seattle, WA
2. Deepmind, Mountain View, CA
3. Goizueta Business School, Emory University, Atlanta, GA
4. Daniels School of Business, Purdue University, West Lafayette, IN
Funder
NSF-SES
National Science Foundation
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2311364
Reference51 articles.
1. “Improved Algorithms for Linear Stochastic Bandits,”;Abbasi-Yadkori Y.;Advances in Neural Information Processing Systems,2011
2. Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values
3. Allen, G. (2012), “Sparse Higher-Order Principal Components Analysis,” in Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 22, 27–36.
4. Exploration–exploitation tradeoff using variance estimates in multi-armed bandits
5. “Using Confidence Bounds for Exploitation-Exploration Trade-offs,”;Auer P.;Journal of Machine Learning Research,2002
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