Improving counterfactual reasoning with kernelised dynamic mixing models von Parbhoo, Sonali, Gottesman, Omer, Ross, Andrew Slavin, Komorowski, Matthieu, Faisal, Aldo, Bon, Isabella, Roth, Volker, Doshi-Velez, Finale

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Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models von Hughes, Michael C, Pradier, Melanie F, Ross, Andrew Slavin, McCoy, Jr, Thomas H, Perlis, Roy H, Doshi-Velez, Finale

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

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

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Right for the Right Reasons: Training Neural Networks to be Interpretable, Robust, and Consistent with Expert Knowledge von Ross, Andrew Slavin

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Training Machine Learning Models by Regularizing their Explanations von Ross, Andrew Slavin

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pyqg Subgrid Forcing Datasets von Ross, Andrew Slavin

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Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement von Ross, Andrew Slavin, Doshi-Velez, Finale

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Learning Predictive and Interpretable Timeseries Summaries from ICU Data von Johnson, Nari, Parbhoo, Sonali, Ross, Andrew Slavin, Doshi-Velez, Finale

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Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement von Andrew Slavin Ross, Doshi-Velez, Finale

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Training Machine Learning Models by Regularizing their Explanations von Andrew Slavin Ross

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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients von Ross, Andrew Slavin, Doshi-Velez, Finale

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Evaluating the Interpretability of Generative Models by Interactive Reconstruction von Ross, Andrew Slavin, Chen, Nina, Hang, Elisa Zhao, Glassman, Elena L, Doshi-Velez, Finale

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Learning Qualitatively Diverse and Interpretable Rules for Classification von Ross, Andrew Slavin, Pan, Weiwei, Doshi-Velez, Finale

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Ensembles of Locally Independent Prediction Models von Ross, Andrew Slavin, Pan, Weiwei, Celi, Leo Anthony, Doshi-Velez, Finale

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Learning Predictive and Interpretable Timeseries Summaries from ICU Data von Johnson, Nari, Parbhoo, Sonali, Andrew Slavin Ross, Doshi-Velez, Finale

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Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations von Ross, Andrew Slavin, Hughes, Michael C, Doshi-Velez, Finale

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Human-in-the-Loop Interpretability Prior von Lage, Isaac, Ross, Andrew Slavin, Kim, Been, Gershman, Samuel J, Doshi-Velez, Finale

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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients von Andrew Slavin Ross, Doshi-Velez, Finale

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Evaluating the Interpretability of Generative Models by Interactive Reconstruction von Andrew Slavin Ross, Chen, Nina, Elisa Zhao Hang, Glassman, Elena L, Doshi-Velez, Finale

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