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Understanding Deep Learning (Still) Requires Rethinking Generalization
From Communications of the ACM

Understanding Deep Learning (Still) Requires Rethinking Generalization

In this work, we presented a simple experimental framework for interrogating purported measures of generalization. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals
From Communications of the ACM | March 2021
Guilt-Free Data Reuse
From Communications of the ACM

Guilt-Free Data Reuse

In this work, we initiate a principled study of how to guarantee the validity of statistical inference in adaptive data analysis. Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth
From Communications of the ACM | April 2017
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