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Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement

Feb 09, 2021
Andrew Slavin Ross, Finale Doshi-Velez

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Evaluating the Interpretability of Generative Models by Interactive Reconstruction

Feb 02, 2021
Andrew Slavin Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, Finale Doshi-Velez

* CHI 2021 accepted paper 

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Ensembles of Locally Independent Prediction Models

Nov 27, 2019
Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez

* This is an expansion of arXiv:1806.08716 with different applications and focus, accepted to AAAI 2020 

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Tackling Climate Change with Machine Learning

Jun 10, 2019
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

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Human-in-the-Loop Interpretability Prior

Oct 30, 2018
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez

* To appear at NIPS 2018, selected for a spotlight. 13 pages (incl references and appendix) 

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Training Machine Learning Models by Regularizing their Explanations

Sep 29, 2018
Andrew Slavin Ross

* Harvard CSE master's thesis; includes portions of arxiv:1703.03717 and arxiv:1711.09404 

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Learning Qualitatively Diverse and Interpretable Rules for Classification

Jul 19, 2018
Andrew Slavin Ross, Weiwei Pan, Finale Doshi-Velez

* Presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden (revision fixes minor issues) 

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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

Nov 26, 2017
Andrew Slavin Ross, Finale Doshi-Velez

* To appear in AAAI 2018 

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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

May 25, 2017
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez

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