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2020 – today
- 2024
[c12]Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst
, Anthony Philippakis, David A. Sontag:
Benchmarking Observational Studies with Experimental Data under Right-Censoring. AISTATS 2024: 4285-4293- [c11]Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder:
Auditing Fairness under Unobserved Confounding. AISTATS 2024: 4339-4347 - [c10]Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst:
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? EMNLP 2024: 12143-12170 - [i12]Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo:
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR abs/2403.01628 (2024) - [i11]Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder:
Auditing Fairness under Unobserved Confounding. CoRR abs/2403.14713 (2024) - 2023
- [b1]Michael Oberst:
Towards Rigorously Tested & Reliable Machine Learning for Health. MIT, USA, 2023 - [c9]Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag:
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. AISTATS 2023: 5869-5898 - [i10]Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag:
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. CoRR abs/2301.13133 (2023) - 2022
- [c8]Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. NeurIPS 2022 - [c7]Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. NeurIPS 2022 - [i9]Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR abs/2205.15947 (2022) - [i8]Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. CoRR abs/2209.13708 (2022) - 2021
- [c6]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML 2021: 8260-8270 - [c5]Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag:
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. NeurIPS 2021: 15328-15343 - [i7]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR abs/2103.02477 (2021) - [i6]Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag:
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. CoRR abs/2110.14508 (2021) - 2020
- [c4]Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. AISTATS 2020: 788-798 - [c3]Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag:
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. KDD 2020: 1937-1947 - [i5]Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca:
ML4H Abstract Track 2019. CoRR abs/2002.01584 (2020) - [i4]Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag:
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. CoRR abs/2006.00927 (2020) - [i3]Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag:
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. CoRR abs/2010.04279 (2020)
2010 – 2019
- 2019
- [c2]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML 2019: 4881-4890 - [c1]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Corey Chivers, Andrew Beam, Tristan Naumann, Brett K. Beaulieu-Jones:
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 2019: 1-9 - [e1]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Brett K. Beaulieu-Jones:
Machine Learning for Health Workshop, ML4H@NeurIPS 2019, Vancouver, BC, Canada, 13 December 2019. Proceedings of Machine Learning Research 116, PMLR 2019 [contents] - [i2]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. CoRR abs/1905.05824 (2019) - [i1]Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney:
Characterization of Overlap in Observational Studies. CoRR abs/1907.04138 (2019)
Coauthor Index
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