| Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks C Nagpal, X Li, A Dubrawski IEEE Journal of Biomedical and Health Informatics 25 (8), 3163-3175, 2021 | 126 | 2021 |
| Deep Cox mixtures for survival regression C Nagpal, S Yadlowsky, N Rostamzadeh, K Heller Machine Learning for Healthcare Conference, 674-708, 2021 | 67 | 2021 |
| An Entity Resolution approach to isolate instances of Human Trafficking online C Nagpal, K Miller, B Boecking, A Dubrawski W-NUT, Empirical Methods in Natural Language Processing (EMNLP) 2017, 2017 | 47 | 2017 |
| Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines C Nagpal, D Wei, B Vinzamuri, M Shekhar, SE Berger, S Das, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 19-29, 2020 | 30* | 2020 |
| Deep Parametric Time-to-Event Regression with Time-Varying Covariates C Nagpal, V Jeanselme, A Dubrawski Survival Prediction-Algorithms, Challenges and Applications, 184-193, 2021 | 24 | 2021 |
| Counterfactual Phenotyping with Censored Time-to-Events C Nagpal, M Goswami, K Dufendach, A Dubrawski ACM Conference on Knowledge Discovery and Data Mining, 2022 | 23 | 2022 |
| Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking J Eisenstein, C Nagpal, A Agarwal, A Beirami, A D'Amour, DJ Dvijotham, ... arXiv preprint arXiv:2312.09244, 2023 | 22 | 2023 |
| auton-survival: An open-source package for regression, counterfactual estimation, evaluation and phenotyping with censored time-to-event data C Nagpal, W Potosnak, A Dubrawski Machine Learning for Healthcare Conference, 585-608, 2022 | 18* | 2022 |
| Nonlinear semi-parametric models for survival analysis C Nagpal, R Sangave, A Chahar, P Shah, A Dubrawski, B Raj arXiv preprint arXiv:1905.05865, 2019 | 10 | 2019 |
| Theoretical guarantees on the best-of-n alignment policy A Beirami, A Agarwal, J Berant, A D'Amour, J Eisenstein, C Nagpal, ... arXiv preprint arXiv:2401.01879, 2024 | 9 | 2024 |
| Participatory Personalization in Classification H James, C Nagpal, KA Heller, B Ustun Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 8* | 2023 |
| A toolbox for surfacing health equity harms and biases in large language models SR Pfohl, H Cole-Lewis, R Sayres, D Neal, M Asiedu, A Dieng, ... arXiv preprint arXiv:2403.12025, 2024 | 5 | 2024 |
| Deep multimodal fusion of health records and notes for multitask clinical event prediction C Nagpal 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017 | 5 | 2017 |
| Robust preference optimization through reward model distillation A Fisch, J Eisenstein, V Zayats, A Agarwal, A Beirami, C Nagpal, P Shaw, ... arXiv preprint arXiv:2405.19316, 2024 | 4 | 2024 |
| Understanding subgroup performance differences of fair predictors using causal models SR Pfohl, N Harris, C Nagpal, D Madras, V Mhasawade, OE Salaudeen, ... NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation …, 2023 | 4 | 2023 |
| Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes C Nagpal, V Sanil, A Dubrawski arXiv preprint arXiv:2302.12504, 2023 | 4 | 2023 |
| Dynamically Personalized Detection of Hemorrhage C Nagpal, X Li, MR Pinsky, A Dubrawski Machine Learning for Healthcare Conference, 109-123, 2019 | 4 | 2019 |
| Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation K Lum, JR Anthis, C Nagpal, A D'Amour arXiv preprint arXiv:2402.12649, 2024 | 3 | 2024 |
| Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis Z Keith, C Nagpal, C Rea, RA Tinguely Journal of Fusion Energy 43 (1), 21, 2024 | 2 | 2024 |
| Transforming and Combining Rewards for Aligning Large Language Models Z Wang, C Nagpal, J Berant, J Eisenstein, A D'Amour, S Koyejo, V Veitch arXiv preprint arXiv:2402.00742, 2024 | 2 | 2024 |
Artur DubrawskiCarnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
