David A. Sontag

211 papers A* 41A 28B 1Misc 8Journal 105Unranked 27

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LLMs can construct powerful representations and streamline sample-efficient supervised learning. CoRR Ilker Demirel, Lawrence Shi, Zeshan M. Hussain, David A. Sontag
2025	—	`conf`	CodingGenie: A Proactive LLM-Powered Programming Assistant. SIGSOFT FSE Companion Sebastian Zhao, Alan Zhu, Hussein Mozannar, David A. Sontag, Ameet Talwalkar, Valerie Chen
2025	J	`jnl`	CodingGenie: A Proactive LLM-Powered Programming Assistant. CoRR Sebastian Zhao, Alan Zhu, Hussein Mozannar, David A. Sontag, Ameet Talwalkar, Valerie Chen
2025	J	`jnl`	Completion ≠ Collaboration: Scaling Collaborative Effort with Agents. CoRR Shannon Zejiang Shen, Valerie Chen, Ken Gu, Alexis Ross, Zixian Ma, Jillian Ross, Alex Gu, Chenglei Si, Wayne Chi, Andi Peng, Jocelyn J. Shen, Ameet Talwalkar, Tongshuang Wu, David A. Sontag
2025	J	`jnl`	DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research. CoRR Rulin Shao, Akari Asai, Shannon Zejiang Shen, Hamish Ivison, Varsha Kishore, Jingming Zhuo, Xinran Zhao, Molly Park, Samuel G. Finlayson, David A. Sontag, Tyler Murray, Sewon Min, Pradeep Dasigi, Luca Soldaini, Faeze Brahman, Wen-tau Yih, Tongshuang Wu, Luke Zettlemoyer, Yoon Kim, Hannaneh Hajishirzi, Pang Wei Koh
2025	J	`jnl`	Diagnosing our datasets: How does my language model learn clinical information? CoRR Furong Jia, David A. Sontag, Monica Agrawal
2025	—	`conf`	How does my language model understand clinical text? CHIL Furong Jia, David A. Sontag, Monica Agrawal
2025	J	`jnl`	Large Language Models are Powerful EHR Encoders. CoRR Stefan Hegselmann, Georg von Arnim, Tillmann Rheude, Noel Kronenberg, David A. Sontag, Gerhard Hindricks, Roland Eils, Benjamin Wild
2025	A*	`conf`	Need Help? Designing Proactive AI Assistants for Programming. CHI Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar
2025	J	`jnl`	The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. Trans. Mach. Learn. Res. Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
2024	—	`conf`	A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models. CHIL Stefan Hegselmann, Shannon Shen, Florian Gierse, Monica Agrawal, David A. Sontag, Xiaoyi Jiang
2024	J	`jnl`	A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models. CoRR Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, Monica Agrawal, David A. Sontag, Xiaoyi Jiang
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`	Deeper evaluation of a single-cell foundation model. Nat. Mac. Intell. Rebecca Boiarsky, Nalini M. Singh, Alejandro Buendia, Ava P. Amini, Gad Getz, David A. Sontag
2024	J	`jnl`	Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology. CoRR Zeshan M. Hussain, Barbara D. Lam, Fernando A. Acosta-Perez, Irbaz B. Riaz, Maia L. Jacobs, Andrew J. Yee, David A. Sontag
2024	J	`jnl`	Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study. CoRR Niklas Mannhardt, Elizabeth Bondi-Kelly, Barbara D. 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
2024	J	`jnl`	Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma. npj Digit. Medicine 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
2024	—	`conf`	Learning to Decode Collaboratively with Multiple Language Models. ACL (1) Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag
2024	J	`jnl`	Learning to Decode Collaboratively with Multiple Language Models. CoRR Shannon Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag
2024	J	`jnl`	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. Sharon Jiang, Barbara D. Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
2024	A*	`conf`	Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. NeurIPS Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David A. Sontag, Ahmed M. Alaa
2024	J	`jnl`	Need Help? Designing Proactive AI Assistants for Programming. CoRR Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar
2024	J	`jnl`	Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. CoRR Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David A. Sontag, Ahmed M. Alaa
2024	A*	`conf`	Prediction-powered Generalization of Causal Inferences. ICML Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
2024	J	`jnl`	Prediction-powered Generalization of Causal Inferences. CoRR Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
2024	J	`jnl`	Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point. CoRR Christina X. Ji, Ahmed M. Alaa, David A. Sontag
2024	J	`jnl`	The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
2024	A*	`conf`	Theoretical Analysis of Weak-to-Strong Generalization. NeurIPS Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2024	J	`jnl`	Theoretical Analysis of Weak-to-Strong Generalization. CoRR Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2023	J	`jnl`	Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks. CoRR Zejiang Shen, Tal August, Pao Siangliulue, Kyle Lo, Jonathan Bragg, Jeff Hammerbacher, Doug Downey, Joseph Chee Chang, David A. Sontag
2023	J	`jnl`	Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration. CoRR Hussein Mozannar, Yuria Utsumi, Irene Y. Chen, Stephanie S. Gervasi, Michele Ewing, Aaron Smith-McLallen, David A. Sontag
2023	—	`conf`	Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. MLHC Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara D. Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
2023	J	`jnl`	Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. CoRR Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara D. Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
2023	A	`conf`	Conformalized Unconditional Quantile Regression. AISTATS Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag
2023	J	`jnl`	Conformalized Unconditional Quantile Regression. CoRR Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag
2023	A*	`conf`	Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. NeurIPS Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
2023	J	`jnl`	Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. CoRR Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
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	—	`conf`	Large-Scale Study of Temporal Shift in Health Insurance Claims. CHIL Christina X. Ji, Ahmed M. Alaa, David A. Sontag
2023	J	`jnl`	Large-Scale Study of Temporal Shift in Health Insurance Claims. CoRR Christina X. Ji, Ahmed M. Alaa, David A. Sontag
2023	A	`conf`	TabLLM: Few-shot Classification of Tabular Data with Large Language Models. AISTATS Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
2023	J	`jnl`	Towards Verifiable Text Generation with Symbolic References. CoRR Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha, Bernhard Gapp, David A. Sontag, Yoon Kim
2023	A	`conf`	Who Should Predict? Exact Algorithms For Learning to Defer to Humans. AISTATS Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
2023	J	`jnl`	Who Should Predict? Exact Algorithms For Learning to Defer to Humans. CoRR Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
2022	A*	`conf`	Clustering Interval-Censored Time-Series for Disease Phenotyping. AAAI Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
2022	A*	`conf`	Co-training Improves Prompt-based Learning for Large Language Models. ICML Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag
2022	J	`jnl`	Co-training Improves Prompt-based Learning for Large Language Models. CoRR Hunter Lang, Monica Agrawal, Yoon Kim, David A. Sontag
2022	A*	`conf`	ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. NeurIPS Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
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
2022	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2022	J	`jnl`	Large Language Models are Zero-Shot Clinical Information Extractors. CoRR Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
2022	A*	`conf`	Large language models are few-shot clinical information extractors. EMNLP Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
2022	A	`conf`	Leveraging Time Irreversibility with Order-Contrastive Pre-training. AISTATS Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag
2022	A*	`conf`	Sample Efficient Learning of Predictors that Complement Humans. ICML Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi
2022	J	`jnl`	Sample Efficient Learning of Predictors that Complement Humans. CoRR Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi
2022	J	`jnl`	TabLLM: Few-shot Classification of Tabular Data with Large Language Models. CoRR Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
2022	A*	`conf`	Teaching Humans When to Defer to a Classifier via Exemplars. AAAI Hussein Mozannar, Arvind Satyanarayan, David A. Sontag
2022	A*	`conf`	Training Subset Selection for Weak Supervision. NeurIPS Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag
2022	J	`jnl`	Training Subset Selection for Weak Supervision. CoRR Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag
2022	A	`conf`	Using time-series privileged information for provably efficient learning of prediction models. AISTATS Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson
2021	A*	`conf`	Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CHI Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
2021	J	`jnl`	Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CoRR Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
2021	A	`conf`	Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. AISTATS Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan
2021	J	`jnl`	Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. CoRR Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan
2021	—	`conf`	CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes. ACL/IJCNLP (1) James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, T. Greg McKelvey, Hui Dai, Yi Yang, David A. Sontag
2021	J	`jnl`	CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes. CoRR James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, T. Greg McKelvey, Hui Dai, Yi Yang, David A. Sontag
2021	J	`jnl`	Clustering Left-Censored Multivariate Time-Series. CoRR Irene Y. Chen, Rahul G. Krishnan, David A. Sontag
2021	A*	`conf`	Deep Contextual Clinical Prediction with Reverse Distillation. AAAI Rohan S. Kodialam, Rebecca Boiarsky, Justin Lim, Aditya Sai, Neil Dixit, David A. Sontag
2021	—	`conf`	Directing Human Attention in Event Localization for Clinical Timeline Creation. MLHC Jason Zhao, Monica Agrawal, Pedram Razavi, 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`	Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). ICML Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2021	J	`jnl`	Leveraging Time Irreversibility with Order-Contrastive Pre-training. CoRR Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag
2021	A*	`conf`	MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. UIST Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
2021	J	`jnl`	MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. CoRR Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
2021	A*	`conf`	Neural Pharmacodynamic State Space Modeling. ICML Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag
2021	J	`jnl`	Neural Pharmacodynamic State Space Modeling. CoRR Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag
2021	A	`conf`	PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka
2021	J	`jnl`	Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media. CoRR Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar, Juan M. Banda, David A. Sontag
2021	J	`jnl`	Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media. J. Biomed. Informatics Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar, Juan M. Banda, 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
2021	J	`jnl`	Teaching Humans When To Defer to a Classifier via Examplars. CoRR Hussein Mozannar, Arvind Satyanarayan, David A. Sontag
2021	J	`jnl`	Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. CoRR Rickard Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik D. Johansson
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	A*	`conf`	Consistent Estimators for Learning to Defer to an Expert. ICML Hussein Mozannar, David A. Sontag
2020	J	`jnl`	Consistent Estimators for Learning to Defer to an Expert. CoRR Hussein Mozannar, David A. Sontag
2020	J	`jnl`	Deep Contextual Clinical Prediction with Reverse Distillation. CoRR Rohan S. Kodialam, Rebecca Boiarsky, David A. Sontag
2020	J	`jnl`	Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department. J. Am. Medical Informatics Assoc. Colby Redfield, Abdulhakim Tlimat, Yoni Halpern, David W. Schoenfeld, Edward Ullman, David A. Sontag, Larry A. Nathanson, Steven Horng
2020	A*	`conf`	Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. ICML Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag
2020	A*	`conf`	Estimation of Bounds on Potential Outcomes For Decision Making. ICML Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
2020	—	`conf`	Fast, Structured Clinical Documentation via Contextual Autocomplete. MLHC Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
2020	J	`jnl`	Fast, Structured Clinical Documentation via Contextual Autocomplete. CoRR Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
2020	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2020	J	`jnl`	Graph cuts always find a global optimum (with a catch). CoRR Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2020	—	`conf`	Knowledge Base Completion for Constructing Problem-Oriented Medical Records. MLHC James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag
2020	J	`jnl`	Knowledge Base Completion for Constructing Problem-Oriented Medical Records. CoRR James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag
2020	J	`jnl`	PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka
2020	—	`conf`	Robust Benchmarking for Machine Learning of Clinical Entity Extraction. MLHC Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
2020	J	`jnl`	Robust Benchmarking for Machine Learning of Clinical Entity Extraction. CoRR Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
2020	Misc	`conf`	Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph. PSB Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
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`	Benefits of Overparameterization in Single-Layer Latent Variable Generative Models. CoRR Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David A. Sontag
2019	A	`conf`	Block Stability for MAP Inference. AISTATS Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
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	J	`jnl`	Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
2019	—	`conf`	Few-Shot Learning for Dermatological Disease Diagnosis. MLHC Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chaplain, David A. Sontag, Xavier Amatriain
2019	J	`jnl`	Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces. Int. J. Medical Informatics Nathaniel R. Greenbaum, Yacine Jernite, Yoni Halpern, Shelley Calder, Larry A. Nathanson, David A. Sontag, Steven Horng
2019	J	`jnl`	Open Set Medical Diagnosis. CoRR Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David A. Sontag, Xavier Amatriain
2019	A	`conf`	Overcomplete Independent Component Analysis via SDP. AISTATS Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag
2019	J	`jnl`	Overcomplete Independent Component Analysis via SDP. CoRR Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag
2019	J	`jnl`	Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph. CoRR Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
2019	A	`conf`	Support and Invertibility in Domain-Invariant Representations. AISTATS Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
2019	J	`jnl`	Support and Invertibility in Domain-Invariant Representations. CoRR Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath
2019	J	`jnl`	Train and Test Tightness of LP Relaxations in Structured Prediction. J. Mach. Learn. Res. Ofer Meshi, Ben London, Adrian Weller, David A. Sontag
2018	J	`jnl`	Block Stability for MAP Inference. CoRR Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2018	Misc	`conf`	Cell-specific prediction and application of drug-induced gene expression . PSB Rachel Hodos, Ping Zhang, Hao-Chih Lee, Qiaonan Duan, Zichen Wang, Neil R. Clark, Avi Ma'ayan, Fei Wang, Brian A. Kidd, Jianying Hu, David A. Sontag, Joel Dudley
2018	J	`jnl`	Evaluating Reinforcement Learning Algorithms in Observational Health Settings. CoRR Omer Gottesman, Fredrik D. Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-Wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David A. Sontag, Finale Doshi-Velez
2018	J	`jnl`	Learning topic models - provably and efficiently. Commun. ACM Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2018	A	`conf`	Max-margin learning with the Bayes factor. UAI Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag
2018	A	`conf`	Optimality of Approximate Inference Algorithms on Stable Instances. AISTATS Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2018	J	`jnl`	Prototypical Clustering Networks for Dermatological Disease Diagnosis. CoRR Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David A. Sontag, Xavier Amatriain
2018	A*	`conf`	Semi-Amortized Variational Autoencoders. ICML Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush
2018	J	`jnl`	Semi-Amortized Variational Autoencoders. CoRR Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush
2018	A*	`conf`	Why Is My Classifier Discriminatory? NeurIPS Irene Y. Chen, Fredrik D. Johansson, David A. Sontag
2018	J	`jnl`	Why Is My Classifier Discriminatory? CoRR Irene Y. Chen, Fredrik D. Johansson, David A. Sontag
2017	J	`jnl`	Alpha-expansion is Exact on Stable Instances. CoRR Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan
2017	—	`conf`	Causal Effect Inference with Deep Latent-Variable Models. NIPS Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Causal Effect Inference with Deep Latent-Variable Models. CoRR Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning. CoRR Yacine Jernite, Samuel R. Bowman, David A. Sontag
2017	—	`conf`	Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. CRI Juan M. Banda, Yoni Halpern, David A. Sontag, Nigam Shah
2017	A*	`conf`	Estimating individual treatment effect: generalization bounds and algorithms. ICML Uri Shalit, Fredrik D. Johansson, David A. Sontag
2017	J	`jnl`	Grounded Recurrent Neural Networks. CoRR Ankit Vani, Yacine Jernite, David A. Sontag
2017	B	`conf`	Objective assessment of depressive symptoms with machine learning and wearable sensors data. ACII Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David A. Sontag, Rosalind W. Picard
2017	A*	`conf`	Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. ICML Yacine Jernite, Anna Choromanska, David A. Sontag
2017	A*	`conf`	Structured Inference Networks for Nonlinear State Space Models. AAAI Rahul G. Krishnan, Uri Shalit, David A. Sontag
2016	J	`jnl`	Bounding and Minimizing Counterfactual Error. CoRR Uri Shalit, Fredrik D. Johansson, David A. Sontag
2016	A*	`conf`	Character-Aware Neural Language Models. AAAI Yoon Kim, Yacine Jernite, David A. Sontag, Alexander M. Rush
2016	—	`conf`	Clinical Tagging with Joint Probabilistic Models. MLHC Yoni Halpern, Steven Horng, David A. Sontag
2016	J	`jnl`	Clinical Tagging with Joint Probabilistic Models. CoRR Yoni Halpern, Steven Horng, David A. Sontag
2016	Misc	`conf`	Comparison of Approaches for Heart Failure Case Identification from EHR Data. AMIA Saul Blecker, Stuart Katz, Leora Horwitz, Gilad J. Kuperman, Hannah Park, David A. Sontag
2016	Misc	`conf`	Data Mining for Medical Informatics (DMMI) - Learning Health. AMIA Fei Wang, Gregor Stiglic, Mihaela van der Schaar, David A. Sontag, Christopher C. Yang
2016	J	`jnl`	Electronic medical record phenotyping using the anchor and learn framework. J. Am. Medical Informatics Assoc. Yoni Halpern, Steven Horng, Youngduck Choi, David A. Sontag
2016	—	`conf`	Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. MLHC Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh
2016	J	`jnl`	Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. CoRR Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh
2016	—	`conf`	Learning Low-Dimensional Representations of Medical Concepts. CRI Youngduck Choi, Chill Yi-I Chiu, David A. Sontag
2016	A*	`conf`	Learning Representations for Counterfactual Inference. ICML Fredrik D. Johansson, Uri Shalit, David A. Sontag
2016	J	`jnl`	Learning Representations for Counterfactual Inference. CoRR Fredrik D. Johansson, Uri Shalit, David A. Sontag
2016	J	`jnl`	Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests. CoRR Narges Razavian, Jake Marcus, David A. Sontag
2016	—	`conf`	Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. MLHC Narges Razavian, Jake Marcus, David A. Sontag
2016	J	`jnl`	Recurrent Neural Networks for Multivariate Time Series with Missing Values. CoRR Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David A. Sontag, Yan Liu
2016	J	`jnl`	Simultaneous Learning of Trees and Representations for Extreme Classification, with Application to Language Modeling. CoRR Yacine Jernite, Anna Choromanska, David A. Sontag, Yann LeCun
2016	J	`jnl`	Structured Inference Networks for Nonlinear State Space Models. CoRR Rahul G. Krishnan, Uri Shalit, David A. Sontag
2016	A	`conf`	Tightness of LP Relaxations for Almost Balanced Models. AISTATS Adrian Weller, Mark Rowland, David A. Sontag
2016	A*	`conf`	Train and Test Tightness of LP Relaxations in Structured Prediction. ICML Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag
2015	A*	`conf`	A Fast Variational Approach for Learning Markov Random Field Language Models. ICML Yacine Jernite, Alexander M. Rush, David A. Sontag
2015	J	`jnl`	Anchored Discrete Factor Analysis. CoRR Yoni Halpern, Steven Horng, David A. Sontag
2015	—	`conf`	Barrier Frank-Wolfe for Marginal Inference. NIPS Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag
2015	J	`jnl`	Barrier Frank-Wolfe for Marginal Inference. CoRR Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag
2015	J	`jnl`	Character-Aware Neural Language Models. CoRR Yoon Kim, Yacine Jernite, David A. Sontag, Alexander M. Rush
2015	J	`jnl`	Deep Kalman Filters. CoRR Rahul G. Krishnan, Uri Shalit, David A. Sontag
2015	Misc	`conf`	Gaussian Processes for interpreting Multiple Prostate Specific Antigen measurements for Prostate Cancer Prediction. AMIA Narges Razavian, Saul Blecker, David A. Sontag
2015	A*	`conf`	How Hard is Inference for Structured Prediction? ICML Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim
2015	J	`jnl`	Incorporating Type II Error Probabilities from Independence Tests into Score-Based Learning of Bayesian Network Structure. CoRR Eliot Brenner, David A. Sontag
2015	J	`jnl`	On the Tightness of LP Relaxations for Structured Prediction. CoRR Ofer Meshi, Mehrdad Mahdavi, David A. Sontag
2015	J	`jnl`	Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors. Big Data Narges Razavian, Saul Blecker, Ann Marie Schmidt, Aaron Smith-McLallen, Somesh Nigam, David A. Sontag
2015	J	`jnl`	Temporal Convolutional Neural Networks for Diagnosis from Lab Tests. CoRR Narges Razavian, David A. Sontag
2015	Misc	`conf`	Visual Exploration of Temporal Data in Electronic Medical Records. AMIA Josua Krause, Narges Razavian, Enrico Bertini, David A. Sontag
2014	—	`conf`	Instance Segmentation of Indoor Scenes Using a Coverage Loss. ECCV (1) Nathan Silberman, David A. Sontag, Rob Fergus
2014	A	`conf`	Lifted Tree-Reweighted Variational Inference. UAI Hung Hai Bui, Tuyen N. Huynh, David A. Sontag
2014	J	`jnl`	Lifted Tree-Reweighted Variational Inference. CoRR Hung Hai Bui, Tuyen N. Huynh, David A. Sontag
2014	J	`jnl`	Tight Error Bounds for Structured Prediction. CoRR Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim
2014	A	`conf`	Understanding the Bethe Approximation: When and How can it go Wrong? UAI Adrian Weller, Kui Tang, Tony Jebara, David A. Sontag
2014	A*	`conf`	Unsupervised learning of disease progression models. KDD Xiang Wang, David A. Sontag, Fei Wang
2014	Misc	`conf`	Using Anchors to Estimate Clinical State without Labeled Data. AMIA Yoni Halpern, Youngduck Choi, Steven Horng, David A. Sontag
2013	—	`conf`	A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2013	—	`conf`	Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. NIPS Yacine Jernite, Yonatan Halpern, David A. Sontag
2013	A	`conf`	SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. UAI Eliot Brenner, David A. Sontag
2013	J	`jnl`	SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. CoRR Eliot Brenner, David A. Sontag
2013	A	`conf`	Unsupervised Learning of Noisy-Or Bayesian Networks. UAI Yonatan Halpern, David A. Sontag
2013	J	`jnl`	Unsupervised Learning of Noisy-Or Bayesian Networks. CoRR Yonatan Halpern, David A. Sontag
2012	J	`jnl`	A Practical Algorithm for Topic Modeling with Provable Guarantees CoRR Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2012	J	`jnl`	Efficiently Searching for Frustrated Cycles in MAP Inference CoRR David A. Sontag, Do Kook Choe, Yitao Li
2012	A	`conf`	Efficiently Searching for Frustrated Cycles in MAP Inference. UAI David A. Sontag, Do Kook Choe, Yitao Li
2012	A	`conf`	Probabilistic models for personalizing web search. WSDM David A. Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan T. Dumais, Bodo Billerbeck
2012	J	`jnl`	Tightening LP Relaxations for MAP using Message Passing CoRR David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss
2011	—	`conf`	Complexity of Inference in Latent Dirichlet Allocation. NIPS David A. Sontag, Daniel M. Roy
2011	A	`conf`	Personalizing web search results by reading level. CIKM Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David A. Sontag
2010	—	`—`	Approximate inference in graphical models using linear programming relaxations. David A. Sontag
2010	A*	`conf`	Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag
2010	A	`conf`	Learning Bayesian Network Structure using LP Relaxations. AISTATS Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila
2010	A*	`conf`	Learning Efficiently with Approximate Inference via Dual Losses. ICML Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson
2010	—	`conf`	More data means less inference: A pseudo-max approach to structured learning. NIPS David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson
2010	A*	`conf`	On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola
2009	A	`conf`	Scaling all-pairs overlay routing. CoNEXT David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger
2009	A	`conf`	Tree Block Coordinate Descent for MAP in Graphical Models. AISTATS David A. Sontag, Tommi S. Jaakkola
2008	—	`conf`	Clusters and Coarse Partitions in LP Relaxations. NIPS David A. Sontag, Amir Globerson, Tommi S. Jaakkola
2008	A	`conf`	Tightening LP Relaxations for MAP using Message Passing. UAI David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss
2007	—	`conf`	New Outer Bounds on the Marginal Polytope. NIPS David A. Sontag, Tommi S. Jaakkola
2007	Misc	`conf`	Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments. Pacific Symposium on Biocomputing David A. Sontag, Rohit Singh, Bonnie Berger
2005	A	`conf`	Approximate Inference for Infinite Contingent Bayesian Networks. AISTATS Brian Milch, Bhaskara Marthi, David A. Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov
2005	—	`conf`	BLOG: Probabilistic Models with Unknown Objects. Probabilistic, Logical and Relational Learning Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov
2005	A*	`conf`	BLOG: Probabilistic Models with Unknown Objects. IJCAI Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov