| Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients AS Ross, F Doshi-Velez Thirty-Second AAAI Conference on Artificial Intelligence, 1660-1669, 2017 | 469 | 2017 |
| Tackling Climate Change with Machine Learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys 55 (2), 1-96, 2019 | 451* | 2019 |
| Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations AS Ross, MC Hughes, Doshi-Velez, Finale Proceedings of the Twenty-Sixth International Joint Conference on Artificial …, 2017 | 397 | 2017 |
| Human-in-the-loop interpretability prior I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez Advances in neural information processing systems 31, 2018 | 116 | 2018 |
| Improving sepsis treatment strategies by combining deep and kernel-based reinforcement learning X Peng, Y Ding, D Wihl, O Gottesman, M Komorowski, LH Lehman, ... AMIA Annual Symposium Proceedings 2018, 887, 2018 | 62 | 2018 |
| Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn. S Idreos, N Dayan, W Qin, M Akmanalp, S Hilgard, A Ross, J Lennon, ... CIDR, 2019 | 58 | 2019 |
| Hydrodynamic irreversibility in particle suspensions with nonuniform strain JS Guasto, AS Ross, JP Gollub Physical Review E 81 (6), 061401, 2010 | 23 | 2010 |
| The neural lasso: Local linear sparsity for interpretable explanations A Ross, I Lage, F Doshi-Velez Workshop on Transparent and Interpretable Machine Learning in Safety …, 2017 | 17 | 2017 |
| Ensembles of locally independent prediction models A Ross, W Pan, L Celi, F Doshi-Velez Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5527-5536, 2020 | 12 | 2020 |
| Evaluating the interpretability of generative models by interactive reconstruction A Ross, N Chen, EZ Hang, EL Glassman, F Doshi-Velez Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021 | 11 | 2021 |
| Learning qualitatively diverse and interpretable rules for classification AS Ross, W Pan, F Doshi-Velez arXiv preprint arXiv:1806.08716, 2018 | 9 | 2018 |
| Improving counterfactual reasoning with kernelised dynamic mixing models S Parbhoo, O Gottesman, AS Ross, M Komorowski, A Faisal, I Bon, ... PloS one 13 (11), e0205839, 2018 | 8 | 2018 |
| Assessment of a prediction model for antidepressant treatment stability using supervised topic models MC Hughes, MF Pradier, AS Ross, TH McCoy, RH Perlis, F Doshi-Velez JAMA network open 3 (5), e205308-e205308, 2020 | 7* | 2020 |
| Learning key-value store design S Idreos, N Dayan, W Qin, M Akmanalp, S Hilgard, A Ross, J Lennon, ... arXiv preprint arXiv:1907.05443, 2019 | 7 | 2019 |
| Benchmarks, algorithms, and metrics for hierarchical disentanglement A Ross, F Doshi-Velez International Conference on Machine Learning, 9084-9094, 2021 | 6 | 2021 |
| Refactoring Machine Learning AS Ross, JZ Forde NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018 | 2 | 2018 |
| GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data N Loose, R Abernathey, I Grooms, J Busecke, A Guillaumin, E Yankovsky, ... Journal of Open Source Software 7 (70), 3947, 2022 | 1 | 2022 |
| Benchmarking of machine learning ocean subgrid parameterizations in an idealized model AS Ross, Z Li, P Perezhogin, C Fernandez-Granda, L Zanna | | 2022 |
| Learning Predictive and Interpretable Timeseries Summaries from ICU Data N Johnson, S Parbhoo, AS Ross, F Doshi-Velez AMIA Annual Symposium Proceedings 2021, 581, 2021 | | 2021 |
| Right for the Right Reasons: Training Neural Networks to Be Interpretable, Robust, and Consistent with Expert Knowledge AS Ross Harvard University, 2021 | | 2021 |
