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Michael Oberst
Michael Oberst
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
M Oberst, D Sontag
International Conference on Machine Learning (ICML) 2019, 2019
872019
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
192020
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
122021
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), 1-13, 2020
112020
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
112020
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
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
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
32020
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
N Thams, M Oberst, D Sontag
arXiv preprint arXiv:2205.15947, 2022
22022
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
22021
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
2022
Counterfactual policy introspection using structural causal models
MK Oberst
Massachusetts Institute of Technology, 2019
2019
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
M Oberst, N Thams, D Sontag
ICML 2022: Workshop on Spurious Correlations, Invariance and Stability, 0
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Articles 1–13