| Beyond sparsity: Tree regularization of deep models for interpretability M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez 32nd AAAI Conference on Artificial Intelligence, 2017 | 377 | 2017 |
| Determinants of HIV-1 reservoir size and long-term dynamics during suppressive ART N Bachmann, C Von Siebenthal, V Vongrad, T Turk, K Neumann, ... Nature communications 10 (1), 3193, 2019 | 209 | 2019 |
| Addressing leakage in concept bottleneck models M Havasi, S Parbhoo, F Doshi-Velez Advances in Neural Information Processing Systems 35, 23386-23397, 2022 | 121 | 2022 |
| Combining kernel and model based learning for HIV therapy selection S Parbhoo, J Bogojeska, M Zazzi, V Roth, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2017, 239, 2017 | 107 | 2017 |
| Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions O Gottesman, J Futoma, Y Liu, S Parbhoo, L Celi, E Brunskill, ... International Conference on Machine Learning, 3658-3667, 2020 | 97 | 2020 |
| Real-time prediction of COVID-19 related mortality using electronic health records P Schwab, A Mehrjou, S Parbhoo, LA Celi, J Hetzel, M Hofer, B Schölkopf, ... Nature communications 12 (1), 1058, 2021 | 80 | 2021 |
| Regional tree regularization for interpretability in black box models M Wu, S Parbhoo, M Hughes, R Kindle, L Celi, M Zazzi, V Roth, ... arXiv preprint arXiv:1908.04494, 2019 | 72* | 2019 |
| Learning-to-defer for sequential medical decision-making under uncertainty S Joshi, S Parbhoo, F Doshi-Velez Transactions on Machine Learning Research, 2021 | 40* | 2021 |
| Optimizing for interpretability in deep neural networks with tree regularization M Wu, S Parbhoo, MC Hughes, V Roth, F Doshi-Velez Journal of Artificial Intelligence Research 72, 1-37, 2021 | 37 | 2021 |
| Preferential mixture-of-experts: Interpretable models that rely on human expertise as much as possible MF Pradier, J Zazo, S Parbhoo, RH Perlis, M Zazzi, F Doshi-Velez AMIA Summits on Translational Science Proceedings 2021, 525, 2021 | 35 | 2021 |
| Information bottleneck for estimating treatment effects with systematically missing covariates S Parbhoo, M Wieser, A Wieczorek, V Roth Entropy 22 (4), 389, 2020 | 31* | 2020 |
| CD8+ T-cell differentiation and dysfunction inform treatment response in acute myeloid leukemia F Mazziotta, L Biavati, J Rimando, S Rutella, N Borcherding, S Parbhoo, ... Blood 144 (11), 1168-1182, 2024 | 28 | 2024 |
| Ncore: Neural counterfactual representation learning for combinations of treatments S Parbhoo, S Bauer, P Schwab arXiv preprint arXiv:2103.11175, 2021 | 21 | 2021 |
| Risk sensitive dead-end identification in safety-critical offline reinforcement learning TW Killian, S Parbhoo, M Ghassemi Transactions on Machine Learning Research (TMLR), 2023 | 15 | 2023 |
| A reinforcement learning design for HIV clinical trials S Parbhoo PQDT-Global, 2014 | 15 | 2014 |
| Transfer Learning from Well-Curated to Less-Resourced Populations with HIV S Parbhoo, M Wieser, V Roth, F Doshi-Velez Proceedings of Machine Learning Research 126, 1-19, 2020 | 13 | 2020 |
| Greedy structure learning of hierarchical compositional models A Kortylewski, A Wieczorek, M Wieser, C Blumer, S Parbhoo, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 13 | 2019 |
| Informed mcmc with bayesian neural networks for facial image analysis A Kortylewski, M Wieser, A Morel-Forster, A Wieczorek, S Parbhoo, ... arXiv preprint arXiv:1811.07969, 2018 | 13 | 2018 |
| Learning optimal summaries of clinical time-series with concept bottleneck models C Wu, S Parbhoo, M Havasi, F Doshi-Velez Machine Learning for Healthcare Conference, 648-672, 2022 | 12 | 2022 |
| 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 |
Finale Doshi-VelezProfessor, HarvardVerified email at seas.harvard.edu
