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pro vyhledávání: '"Gil, Fernando Amat"'
Autor:
LeVine, Will, Pikus, Benjamin, Phillips, Jacob, Norman, Berk, Gil, Fernando Amat, Hendryx, Sean
As deep neural networks become adopted in high-stakes domains, it is crucial to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence -- ultima
Externí odkaz:
http://arxiv.org/abs/2401.12129
Calibration of deep learning models is crucial to their trustworthiness and safe usage, and as such, has been extensively studied in supervised classification models, with methods crafted to decrease miscalibration. However, there has yet to be a com
Externí odkaz:
http://arxiv.org/abs/2303.12748
Publikováno v:
NeurIPS 2021
We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging bec
Externí odkaz:
http://arxiv.org/abs/2106.07914
We study the problem of off-policy evaluation for slate bandits, for the typical case in which the logging policy factorizes over the slots of the slate. We slightly depart from the existing literature by taking Bayes risk as the criterion by which t
Externí odkaz:
http://arxiv.org/abs/2101.02553