Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)

no code implementations • 8 Aug 2017 • Been Kim, Dmitry M. Malioutov, Kush R. Varshney, Adrian Weller

This is the Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10, 2017.

BIG-bench Machine Learning

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

no code implementations • 8 Jul 2016 • Been Kim, Dmitry M. Malioutov, Kush R. Varshney

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.

BIG-bench Machine Learning

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations • 18 Jun 2016 • Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules.

BIG-bench Machine Learning Classification +3

Interpretable Two-level Boolean Rule Learning for Classification

no code implementations • 23 Nov 2015 • Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov

Experiments show that the two-level rules can yield noticeably better performance than one-level rules due to their dramatically larger modeling capacity, and the two algorithms based on the Hamming distance formulation are generally superior to the other two-level rule learning methods in our comparison.

Classification General Classification +1