arXiv Analytics

Sign in

arXiv:2006.10940 [cs.LG]AbstractReferencesReviewsResources

Open Problem: Model Selection for Contextual Bandits

Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo

Published 2020-06-19Version 1

In statistical learning, algorithms for model selection allow the learner to adapt to the complexity of the best hypothesis class in a sequence. We ask whether similar guarantees are possible for contextual bandit learning.

Comments: COLT 2020 open problem
Categories: cs.LG, stat.ML
Related articles: Most relevant | Search more
arXiv:1207.4112 [cs.LG] (Published 2012-07-11)
Algebraic Statistics in Model Selection
arXiv:1607.07959 [cs.LG] (Published 2016-07-27)
Using Kernel Methods and Model Selection for Prediction of Preterm Birth
Ilia Vovsha et al.
arXiv:1909.07140 [cs.LG] (Published 2019-09-16)
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning

Links

Toolbox