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arXiv:1607.02531stat.ML

Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)

Been Kim, Dmitry M. Malioutov, Kush R. Varshney

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Abstract

This is the Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016. Invited speakers were Susan Athey, Rich Caruana, Jacob Feldman, Percy Liang, and Hanna Wallach.

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DOI
Subjects
stat.MLMachine Learning (Statistics) cs.LGMachine Learning

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  1. You can probably use deep learning even if you don't have a lot of data 1 comment

    • This is an area that fades in and out of focus with such venues as the Workshop on Human Interpretability in Machine Learning (WHI) [1]. It's becoming increasingly important when it comes to auditability and understanding of what is actually learned by algorithm. Avoiding…

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