Articles with public access mandates - Michael OberstLearn more
Available somewhere: 9
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
M Oberst, D Sontag
International Conference on Machine Learning (ICML) 2019, 2019
Mandates: US Department of Defense
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
Mandates: US National Science Foundation
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
Mandates: Knut and Alice Wallenberg Foundation
Regularizing towards causal invariance: Linear models with proxies
M Oberst, N Thams, J Peters, D Sontag
International Conference on Machine Learning, 8260-8270, 2021
Mandates: US Department of Defense, Villum Foundation, Carlsberg Foundation DK
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
Mandates: US National Science Foundation, US Department of Defense
Falsification before Extrapolation in Causal Effect Estimation
Z Hussain, M Oberst, MC Shih, D Sontag
Neural Information Processing Systems (NeurIPS) 2022, 2022
Mandates: US Department of Defense, US National Institutes of Health
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
Mandates: US Department of Defense, US National Institutes of Health
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
N Thams, M Oberst, D Sontag
Neural Information Processing Systems (NeurIPS) 2022, 2022
Mandates: US Department of Defense, Villum Foundation
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
Mandates: US National Science Foundation, US Department of Defense
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