Learning Qualitatively Diverse and Interpretable Rules for Classification

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Regularization and variable selection via the elastic net

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Intelligible models for classification and regression

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Deep learning of representations: Looking forward

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Deep feature selection: Theory and application to identify enhancers and promoters

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Interpretable decision sets: A joint framework for description and prediction

Lakkaraju, Himabindu, Bach, Stephen H., and Leskovec, Jure · 2016

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Why should I trust you?: Explaining the predictions of any classifier

Ribeiro, Marco Tulio, Singh, Sameer, and Guestrin, Carlos · 2016

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Enumerate lasso solutions for feature selection

Hara, Satoshi and Maehara, Takanori · 2017

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Multiple similarly-well solutions exist for biomedical feature selection and classification problems

Liu, Jiamei, Xu, Cheng, Yang, Weifeng, Shu, Yayun, Zheng, Weiwei, and Zhou, Fengfeng · 2017

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dsprites: Disentanglement testing sprites dataset

Matthey, Loic, Higgins, Irina, Hassabis, Demis, and Lerchner, Alexander · 2017

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Feature visualization

Olah, Chris, Mordvintsev, Alexander, and Schubert, Ludwig · 2017