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Michael Oberst
Michael Oberst
Johns Hopkins
Подтвержден адрес электронной почты в домене jhu.edu - Главная страница
Название
Процитировано
Процитировано
Год
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
M Oberst, D Sontag
International Conference on Machine Learning (ICML) 2019, 2019
1432019
A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
S Kanjilal, M Oberst, S Boominathan, H Zhou, DC Hooper, D Sontag
Science translational medicine 12 (568), eaay5067, 2020
562020
Characterization of Overlap in Observational Studies
M Oberst, FD Johansson, D Wei, T Gao, G Brat, D Sontag, KR Varshney
23rd International Conference on Artificial Intelligence and Statistics …, 2020
272020
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
242021
Predicting human health from biofluid-based metabolomics using machine learning
ED Evans, C Duvallet, ND Chu, MK Oberst, MA Murphy, I Rockafellow, ...
Scientific reports 10 (1), 17635, 2020
222020
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
S Boominathan, M Oberst, H Zhou, S Kanjilal, D Sontag
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020
152020
Falsification before Extrapolation in Causal Effect Estimation
Z Hussain, M Oberst, MC Shih, D Sontag
Neural Information Processing Systems (NeurIPS) 2022, 2022
62022
Finding regions of heterogeneity in decision-making via expected conditional covariance
J Lim, CX Ji, M Oberst, S Blecker, L Horwitz, D Sontag
Advances in Neural Information Processing Systems 34, 15328-15343, 2021
62021
Falsification of internal and external validity in observational studies via conditional moment restrictions
Z Hussain, MC Shih, M Oberst, I Demirel, D Sontag
International Conference on Artificial Intelligence and Statistics, 5869-5898, 2023
52023
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
N Thams, M Oberst, D Sontag
Neural Information Processing Systems (NeurIPS) 2022, 2022
52022
Trajectory inspection: A method for iterative clinician-driven design of reinforcement learning studies
CX Ji, M Oberst, S Kanjilal, D Sontag
AMIA Summits on Translational Science Proceedings 2021, 305, 2021
42021
Machine Learning for Health (ML4H) 2019: What Makes Machine Learning in Medicine Different?
AV Dalca, MBA McDermott, E Alsentzer, SG Finlayson, M Oberst, F Falck, ...
Machine Learning for Health Workshop, 1-9, 2020
42020
AMR-UTI: Antimicrobial Resistance in Urinary Tract Infections (version 1.0.0)
M Oberst, S Boominathan, H Zhou, S Kanjilal, D Sontag
PhysioNet, 2020
42020
Bias-robust Integration of Observational and Experimental Estimators
M Oberst, A D’Amour, M Chen, Y Wang, D Sontag, S Yadlowsky
arXiv preprint arXiv:2205.10467, 2022
32022
Benchmarking observational studies with experimental data under right-censoring
I Demirel, E De Brouwer, ZM Hussain, M Oberst, AA Philippakis, D Sontag
International Conference on Artificial Intelligence and Statistics, 4285-4293, 2024
22024
Understanding the risks and rewards of combining unbiased and possibly biased estimators, with applications to causal inference
M Oberst, A D'Amour, M Chen, Y Wang, D Sontag, S Yadlowsky
arXiv preprint arXiv:2205.10467, 2022
12022
Auditing Fairness under Unobserved Confounding
Y Byun, D Sam, M Oberst, Z Lipton, B Wilder
International Conference on Artificial Intelligence and Statistics, 4339-4347, 2024
2024
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
H Jeong, S Jabbour, Y Yang, R Thapta, H Mozannar, WJ Han, ...
arXiv preprint arXiv:2403.01628, 2024
2024
Towards Rigorously Tested & Reliable Machine Learning for Health
MK Oberst
Massachusetts Institute of Technology, 2023
2023
Counterfactual policy introspection using structural causal models
MK Oberst
Massachusetts Institute of Technology, 2019
2019
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Статьи 1–20