Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations. - Andrew Slavin Ross; Michael C. Hughes; Finale Doshi-Velez (IJCAI, 2017)

Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients. - Andrew Slavin Ross; Finale Doshi-Velez (AAAI, 2018)

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning. - Xuefeng Peng; Yi Ding; David Wihl; Omer Gottesman; Matthieu Komorowski; Li-Wei H. Lehman; Andrew Slavin Ross; Aldo Faisal; Finale Doshi-Velez (AMIA, 2018)

Human-in-the-Loop Interpretability Prior. - Isaac Lage; Andrew Slavin Ross; Samuel J. Gershman; Been Kim; Finale Doshi-Velez (NeurIPS, 2018)

Learning Qualitatively Diverse and Interpretable Rules for Classification. - Andrew Slavin Ross; Weiwei Pan; Finale Doshi-Velez (CoRR, 2018)

Training Machine Learning Models by Regularizing their Explanations. - Andrew Slavin Ross (CoRR, 2018)

Ensembles of Locally Independent Prediction Models. - Andrew Slavin Ross; Weiwei Pan; Leo A. Celi; Finale Doshi-Velez (AAAI, 2020)

Learning Predictive and Interpretable Timeseries Summaries from ICU Data. - Nari Johnson; Sonali Parbhoo; Andrew Slavin Ross; Finale Doshi-Velez (AMIA, 2021)

Evaluating the Interpretability of Generative Models by Interactive Reconstruction. - Andrew Slavin Ross; Nina Chen; Elisa Zhao Hang; Elena L. Glassman; Finale Doshi-Velez (CHI, 2021)

Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement. - Andrew Slavin Ross; Finale Doshi-Velez (ICML, 2021)

GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data. - Nora Loose; Ryan Abernathey; Ian G. Grooms; Julius Busecke; Arthur Guillaumin; Elizabeth Yankovsky; Gustavo Marques; Jacob Steinberg; Andrew Slavin Ross; Hemant Khatri; Scott Bachman; Laure Zanna; et al. (J. Open Source Softw., 2022)

Tackling Climate Change with Machine Learning. - 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 Sasha Luccioni; Tegan Maharaj; et al. (ACM Comput. Surv., 2023)