Michael Oberst

According to our database, Michael Oberst authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Generate to Discriminate: Expert Routing for Continual Learning.

Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary C. Lipton

CoRR, 2024

The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models.

Daniel P. Jeong, Pranav Mani, Saurabh Garg, Zachary C. Lipton, Michael Oberst

CoRR, 2024

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium.

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

CoRR, 2024

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?

Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Benchmarking Observational Studies with Experimental Data under Right-Censoring.

Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Auditing Fairness under Unobserved Confounding.

Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Towards Rigorously Tested & Reliable Machine Learning for Health

Michael Oberst

PhD thesis, 2023

Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions.

Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Evaluating Robustness to Dataset Shift via Parametric Robustness Sets.

Nikolaj Thams, Michael Oberst, David A. Sontag

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Falsification before Extrapolation in Causal Effect Estimation.

Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance.

Justin Lim, Christina X. Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David A. Sontag

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Regularizing towards Causal Invariance: Linear Models with Proxies.

Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies.

Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag

CoRR, 2020

ML4H Abstract Track 2019.

Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca

CoRR, 2020

Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes.

Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Characterization of Overlap in Observational Studies.

Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different?

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

Proceedings of the Machine Learning for Health Workshop, 2019

Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models.

Michael Oberst, David A. Sontag

Proceedings of the 36th International Conference on Machine Learning, 2019