Michael Oberst

34 papers A* 7A 5Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Expert Routing with Synthetic Data for Domain Incremental Learning. Trans. Mach. Learn. Res. Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary Chase Lipton
2025	A	`conf`	Just Trial Once: Ongoing Causal Validation of Machine Learning Models. UAI Jacob M. Chen, Michael Oberst
2025	J	`jnl`	No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference. CoRR Pranav Mani, Peng Xu, Zachary C. Lipton, Michael Oberst
2025	J	`jnl`	Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context. CoRR Andrew Wang, Jiashuo Zhang, Michael Oberst
2025	J	`jnl`	Towards Virtual Clinical Trials of Radiology AI with Conditional Generative Modeling. CoRR Benjamin D. Killeen, Bohua Wan, Aditya V. Kulkarni, Nathan Drenkow, Michael Oberst, Paul H. Yi, Mathias Unberath
2024	A	`conf`	Auditing Fairness under Unobserved Confounding. AISTATS Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder
2024	J	`jnl`	Auditing Fairness under Unobserved Confounding. CoRR Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder
2024	A	`conf`	Benchmarking Observational Studies with Experimental Data under Right-Censoring. AISTATS Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag
2024	J	`jnl`	Generate to Discriminate: Expert Routing for Continual Learning. CoRR Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary C. Lipton
2024	A*	`conf`	Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? EMNLP Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst
2024	J	`jnl`	Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? CoRR Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst
2024	J	`jnl`	Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR 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
2024	J	`jnl`	The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models. CoRR Daniel P. Jeong, Pranav Mani, Saurabh Garg, Zachary C. Lipton, Michael Oberst
2023	A	`conf`	Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. AISTATS Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag
2023	J	`jnl`	Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. CoRR Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag
2023	—	`—`	Towards Rigorously Tested & Reliable Machine Learning for Health Michael Oberst
2022	A*	`conf`	Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. NeurIPS Nikolaj Thams, Michael Oberst, David A. Sontag
2022	J	`jnl`	Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR Nikolaj Thams, Michael Oberst, David A. Sontag
2022	A*	`conf`	Falsification before Extrapolation in Causal Effect Estimation. NeurIPS Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
2022	J	`jnl`	Falsification before Extrapolation in Causal Effect Estimation. CoRR Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
2021	A*	`conf`	Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. NeurIPS Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag
2021	J	`jnl`	Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. CoRR Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag
2021	A*	`conf`	Regularizing towards Causal Invariance: Linear Models with Proxies. ICML Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2021	J	`jnl`	Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
2020	A	`conf`	Characterization of Overlap in Observational Studies. AISTATS Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney
2020	J	`jnl`	ML4H Abstract Track 2019. CoRR Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca
2020	J	`jnl`	Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. CoRR Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag
2020	A*	`conf`	Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. KDD Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
2020	J	`jnl`	Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. CoRR Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
2019	J	`jnl`	Characterization of Overlap in Observational Studies. CoRR Fredrik D. Johansson, Dennis Wei, Michael Oberst, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney
2019	A*	`conf`	Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML Michael Oberst, David A. Sontag
2019	J	`jnl`	Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. CoRR Michael Oberst, David A. Sontag
2019	—	`conf`	Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 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
2019	—	`ed.`	Machine Learning for Health Workshop, ML4H@NeurIPS 2019, Vancouver, BC, Canada, 13 December 2019 ML4H@NeurIPS Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Brett K. Beaulieu-Jones