| 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 | 250 | 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 | 203 | 2017 |
| Tackling Climate Change with Machine Learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433 [cs, stat], 2019 | 156* | 2019 |
| Human-in-the-loop interpretability prior I Lage, AS Ross, B Kim, SJ Gershman, F Doshi-Velez arXiv preprint arXiv:1805.11571, 2018 | 56 | 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 | 26 | 2019 |
| 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 | 25 | 2018 |
| Hydrodynamic irreversibility in particle suspensions with nonuniform strain JS Guasto, AS Ross, JP Gollub Physical Review E 81 (6), 061401, 2010 | 21 | 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 | 7 | 2017 |
| Learning qualitatively diverse and interpretable rules for classification AS Ross, W Pan, F Doshi-Velez arXiv preprint arXiv:1806.08716, 2018 | 6 | 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 | 5 | 2018 |
| 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 | 3 | 2020 |
| 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 | 1 | 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 | 1 | 2019 |
| Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement AS Ross, F Doshi-Velez arXiv preprint arXiv:2102.05185, 2021 | | 2021 |
| Evaluating the Interpretability of Generative Models by Interactive Reconstruction AS Ross, N Chen, EZ Hang, EL Glassman, F Doshi-Velez arXiv preprint arXiv:2102.01264, 2021 | | 2021 |
| Generating interpretable predictions about antidepressant treatment stability using supervised topic models MC Hughes, MF Pradier, AS Ross, TH McCoy, RH Perlis, F Doshi-Velez medRxiv, 2020 | | 2020 |
| 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 | | 2019 |
| Refactoring Machine Learning AS Ross, JZ Forde NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018 | | 2018 |
| Training Machine Learning Models by Regularizing their Explanations AS Ross arXiv preprint arXiv:1810.00869, 2018 | | 2018 |
| CS265 Final Project: LSM-tree gradient descent M Akmanalp, AS Hilgard, A Ross | | 2017 |
