David A. Sontag

Publication Activity (10 Years)

Years Active: 2005-2024
Publications (10 Years): 146

Top Venues

CoRR

ICML

AISTATS

MLHC

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Niklas Mannhardt, Elizabeth Bondi-Kelly, Barbara Lam, Chloe O'Connell, Mercy Asiedu, Hussein Mozannar, Monica Agrawal, Alejandro Buendia, Tatiana Urman, Irbaz B. Riaz, Catherine E. Ricciardi, Marzyeh Ghassemi, David A. Sontag
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study. CoRR (2024)
Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag
Learning to Decode Collaboratively with Multiple Language Models. ACL (1) (2024)
Sharon Jiang, Barbara D. Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing. J. Am. Medical Informatics Assoc. 31 (7) (2024)
Zeshan M. Hussain, Barbara D. Lam, Fernando A. Acosta-Perez, Irbaz B. Riaz, Maia L. Jacobs, Andrew J. Yee, David A. Sontag
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology. CoRR (2024)
Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
Prediction-powered Generalization of Causal Inferences. CoRR (2024)
Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David A. Sontag, Ahmed M. Alaa
Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. CoRR (2024)
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)
Shannon Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag
Learning to Decode Collaboratively with Multiple Language Models. CoRR (2024)
Christina X. Ji, Ahmed M. Alaa, David A. Sontag
Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point. CoRR (2024)
Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, Monica Agrawal, David A. Sontag, Xiaoyi Jiang
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models. CoRR (2024)
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR (2024)
Zeshan M. Hussain, Edward De Brouwer, Rebecca Boiarsky, Sama Setty, Neeraj Gupta, Guohui Liu, Cong Li, Jaydeep Srimani, Jacob Zhang, Rich Labotka, David A. Sontag
Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma. npj Digit. Medicine 7 (1) (2024)
Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
Theoretical Analysis of Weak-to-Strong Generalization. CoRR (2024)
Christina X. Ji, Ahmed M. Alaa, David A. Sontag
Large-Scale Study of Temporal Shift in Health Insurance Claims. CHIL (2023)
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 (2023)
Ahmed M. Alaa, Zeshan Hussain, David A. Sontag
Conformalized Unconditional Quantile Regression. CoRR (2023)
Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag
Conformalized Unconditional Quantile Regression. AISTATS (2023)
Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. MLHC (2023)
Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. NeurIPS (2023)
Christina X. Ji, Ahmed M. Alaa, David A. Sontag
Large-Scale Study of Temporal Shift in Health Insurance Claims. CoRR (2023)
Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. CoRR (2023)
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)
Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha, Bernhard Gapp, David A. Sontag, Yoon Kim
Towards Verifiable Text Generation with Symbolic References. CoRR (2023)
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. CoRR (2023)
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. AISTATS (2023)
Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. CoRR (2023)
Hussein Mozannar, Yuria Utsumi, Irene Y. Chen, Stephanie S. Gervasi, Michele Ewing, Aaron Smith-McLallen, David A. Sontag
Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration. CoRR (2023)
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. AISTATS (2023)
Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. NeurIPS (2022)
Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson
Using time-series privileged information for provably efficient learning of prediction models. AISTATS (2022)
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. CoRR (2022)
Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag
Training Subset Selection for Weak Supervision. NeurIPS (2022)
Hussein Mozannar, Arvind Satyanarayan, David A. Sontag
Teaching Humans When to Defer to a Classifier via Exemplars. AAAI (2022)
Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag
Leveraging Time Irreversibility with Order-Contrastive Pre-training. AISTATS (2022)
Nikolaj Thams, Michael Oberst, David A. Sontag
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR (2022)
Nikolaj Thams, Michael Oberst, David A. Sontag
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. NeurIPS (2022)
Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
Falsification before Extrapolation in Causal Effect Estimation. CoRR (2022)
Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag
Co-training Improves Prompt-based Learning for Large Language Models. ICML (2022)
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
Large Language Models are Zero-Shot Clinical Information Extractors. CoRR (2022)
Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag
Falsification before Extrapolation in Causal Effect Estimation. NeurIPS (2022)
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. 23 (2022)
Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi
Sample Efficient Learning of Predictors that Complement Humans. CoRR (2022)
Hunter Lang, Monica Agrawal, Yoon Kim, David A. Sontag
Co-training Improves Prompt-based Learning for Large Language Models. CoRR (2022)
Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag
Training Subset Selection for Weak Supervision. CoRR (2022)
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
Large language models are few-shot clinical information extractors. EMNLP (2022)
Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
Clustering Interval-Censored Time-Series for Disease Phenotyping. AAAI (2022)
Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi
Sample Efficient Learning of Predictors that Complement Humans. ICML (2022)
Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar, Juan M. Banda, David A. Sontag
Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media. J. Biomed. Informatics 120 (2021)
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CoRR (2021)
Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. UIST (2021)
Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). ICML (2021)
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 (2021)
Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag
Leveraging Time Irreversibility with Order-Contrastive Pre-training. CoRR (2021)
James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, T. Greg McKelvey, Hui Dai, Yi Yang, David A. Sontag
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes. CoRR (2021)
Zeshan Hussain, Rahul G. Krishnan, David A. Sontag
Neural Pharmacodynamic State Space Modeling. CoRR (2021)
Zeshan Hussain, Rahul G. Krishnan, David A. Sontag
Neural Pharmacodynamic State Space Modeling. ICML (2021)
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)
Rohan S. Kodialam, Rebecca Boiarsky, Justin Lim, Aditya Sai, Neil Dixit, David A. Sontag
Deep Contextual Clinical Prediction with Reverse Distillation. AAAI (2021)
Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
Clustering Left-Censored Multivariate Time-Series. CoRR (2021)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS (2021)
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CHI (2021)
Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. AISTATS (2021)
Rickard Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik D. Johansson
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. CoRR (2021)
Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar, Juan M. Banda, David A. Sontag
Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media. CoRR (2021)
Jason Zhao, Monica Agrawal, Pedram Razavi, David A. Sontag
Directing Human Attention in Event Localization for Clinical Timeline Creation. MLHC (2021)
Hussein Mozannar, Arvind Satyanarayan, David A. Sontag
Teaching Humans When To Defer to a Classifier via Examplars. CoRR (2021)
James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, T. Greg McKelvey, Hui Dai, Yi Yang, David A. Sontag
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes. ACL/IJCNLP (1) (2021)
Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. CoRR (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML (2021)
Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. CoRR (2021)
Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR (2021)
Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. CoRR (2020)
Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. KDD (2020)
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR (2020)
James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag
Knowledge Base Completion for Constructing Problem-Oriented Medical Records. CoRR (2020)
Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
Graph cuts always find a global optimum (with a catch). CoRR (2020)
Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
Estimation of Bounds on Potential Outcomes For Decision Making. ICML (2020)
Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
Fast, Structured Clinical Documentation via Contextual Autocomplete. CoRR (2020)
Christina X. Ji, Michael Oberst, Sanjat Kanjilal, David A. Sontag
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. CoRR (2020)
Colby Redfield, Abdulhakim Tlimat, Yoni Halpern, David W. Schoenfeld, Edward Ullman, David A. Sontag, Larry A. Nathanson, Steven Horng
Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department. J. Am. Medical Informatics Assoc. 27 (1) (2020)
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. CoRR (2020)
Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph. PSB (2020)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR (2020)
Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David A. Sontag, Kush R. Varshney
Characterization of Overlap in Observational Studies. AISTATS (2020)
James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag
Knowledge Base Completion for Constructing Problem-Oriented Medical Records. MLHC (2020)
Hussein Mozannar, David A. Sontag
Consistent Estimators for Learning to Defer to an Expert. CoRR (2020)
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. MLHC (2020)
Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
Fast, Structured Clinical Documentation via Contextual Autocomplete. MLHC (2020)
Rohan S. Kodialam, Rebecca Boiarsky, David A. Sontag
Deep Contextual Clinical Prediction with Reverse Distillation. CoRR (2020)
Hussein Mozannar, David A. Sontag
Consistent Estimators for Learning to Defer to an Expert. ICML (2020)
Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. ICML (2020)
Nathaniel R. Greenbaum, Yacine Jernite, Yoni Halpern, Shelley Calder, Larry A. Nathanson, David A. Sontag, Steven Horng
Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces. Int. J. Medical Informatics 132 (2019)
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David A. Sontag, Xavier Amatriain
Open Set Medical Diagnosis. CoRR (2019)
Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph. CoRR (2019)
Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag
Overcomplete Independent Component Analysis via SDP. AISTATS (2019)
Michael Oberst, David A. Sontag
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML (2019)
Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
Block Stability for MAP Inference. AISTATS (2019)
Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR (2019)
Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
Support and Invertibility in Domain-Invariant Representations. CoRR (2019)
Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag
Overcomplete Independent Component Analysis via SDP. CoRR (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

David A. Sontag