One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees
Author:
Affiliation:
1. Simon Fraser University, Burnaby, BC, Canada
2. Alibaba Group, Seattle, WA, USA
3. Alibaba Group, Hangzhou, China
Funder
Mitacs
NSERC
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3514221.3517900
Reference45 articles.
1. Loan dataset. https://www.kaggle.com/skihikingkevin/online-p2p-lending. Loan dataset. https://www.kaggle.com/skihikingkevin/online-p2p-lending.
2. Congressional samples for approximate answering of group-by queries
3. Knowing when you're wrong
4. BlinkDB
5. P. Auer , N. Cesa-Bianchi , and P. Fischer . Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2--3):235--256 , 2002 . P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2--3):235--256, 2002.
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1. One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees;Proceedings of the 2022 International Conference on Management of Data;2022-06-10
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