Stebėti
Chirag Nagpal
Chirag Nagpal
Research Scientist, Google
Patvirtintas el. paštas cs.cmu.edu - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
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
1052021
Deep Cox mixtures for survival regression
C Nagpal, S Yadlowsky, N Rostamzadeh, K Heller
Machine Learning for Healthcare Conference, 674-708, 2021
542021
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
442017
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
25*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
242021
Counterfactual Phenotyping with Censored Time-to-Events
C Nagpal, M Goswami, K Dufendach, A Dubrawski
ACM Conference on Knowledge Discovery and Data Mining, 2022
172022
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
15*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
102019
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
92023
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
52017
Dynamically Personalized Detection of Hemorrhage
C Nagpal, X Li, MR Pinsky, A Dubrawski
Machine Learning for Healthcare Conference, 109-123, 2019
42019
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
32023
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
22024
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
22023
Participatory Personalization in Classification
H James, C Nagpal, KA Heller, B Ustun
Thirty-seventh Conference on Neural Information Processing Systems, 2023
2*2023
INTERPRETABLE TREATMENT PRIORITIZATION RULE DEFINES DIABETIC PATIENTS THAT BENEFIT FROM PROMPT CORONARY REVASCULARIZATION
C Nagpal, A Dubrawski
Journal of the American College of Cardiology 81 (8_Supplement), 2263-2263, 2023
12023
205: Accuracy Of Identifying Venous Thromboembolism By Administrative Coding Compared To Manual Review
T Pellathy, M Saul, G Clermont, C Nagpal, A Dubrawski, M Pinsky, ...
Critical Care Medicine 46 (1), 85, 2018
12018
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
2024
Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis
Z Keith, C Nagpal, C Rea, RA Tinguely
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
2024
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