| 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 | 1126* | 2019 |
| 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 | 754 | 2017 |
| 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 | 627 | 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 | 168 | 2018 |
| Improving sepsis treatment strategies by combining deep and kernel-based reinforcement learning X Peng, Y Ding, D Wihl, O Gottesman, M Komorowski, HL Li-wei, A Ross, ... AMIA Annual Symposium Proceedings 2018, 887, 2018 | 100 | 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 | 88 | 2019 |
| 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 | 50 | 2021 |
| Benchmarking of machine learning ocean subgrid parameterizations in an idealized model A Ross, Z Li, P Perezhogin, C Fernandez‐Granda, L Zanna Journal of Advances in Modeling Earth Systems 15 (1), 2023 | 49 | 2023 |
| 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 | 41 | 2020 |
| Hydrodynamic irreversibility in particle suspensions with nonuniform strain JS Guasto, AS Ross, JP Gollub Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 81 (6 …, 2010 | 28 | 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 | 25 | 2017 |
| Benchmarks, algorithms, and metrics for hierarchical disentanglement A Ross, F Doshi-Velez International Conference on Machine Learning, 9084-9094, 2021 | 18 | 2021 |
| 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), 2022 | 16 | 2022 |
| 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 | 16 | 2020 |
| Learning qualitatively diverse and interpretable rules for classification AS Ross, W Pan, F Doshi-Velez arXiv preprint arXiv:1806.08716, 2018 | 14 | 2018 |
| 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 | 10 | 2019 |
| 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 | 10 | 2018 |
| 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 | 5 | 2021 |
| Refactoring Machine Learning AS Ross, JZ Forde NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018 | 2 | 2018 |
| Controlled Direct Effect Priors for Bayesian Neural Networks J Du, AS Ross, Y Shavit, F Doshi-Velez NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019 | 1 | 2019 |