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ORCIDDavid A. Sontag
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- affiliation: MIT, Cambridge, MA, USA
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2020 – today
- 2025
[c101]Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar:
Need Help? Designing Proactive AI Assistants for Programming. CHI 2025: 881:1-881:18- [i90]Stefan Hegselmann, Georg von Arnim, Tillmann Rheude, Noel Kronenberg
, David A. Sontag, Gerhard Hindricks, Roland Eils, Benjamin Wild:
Large Language Models are Powerful EHR Encoders. CoRR abs/2502.17403 (2025) - [i89]Sebastian Zhao, Alan Zhu, Hussein Mozannar, David A. Sontag, Ameet Talwalkar, Valerie Chen:
CodingGenie: A Proactive LLM-Powered Programming Assistant. CoRR abs/2503.14724 (2025) - 2024
- [j10]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): 1578-1582 (2024) - [j9]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) - [c100]Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag:
Learning to Decode Collaboratively with Multiple Language Models. ACL (1) 2024: 12974-12990 - [c99]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 - [c98]Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag:
Prediction-powered Generalization of Causal Inferences. ICML 2024 - [c97]Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David A. Sontag, Ahmed M. Alaa:
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. NeurIPS 2024 - [c96]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Theoretical Analysis of Weak-to-Strong Generalization. NeurIPS 2024 - [i88]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:
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study. CoRR abs/2401.09637 (2024) - [i87]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 abs/2402.15422 (2024) - [i86]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 abs/2403.00177 (2024) - [i85]Shannon Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag:
Learning to Decode Collaboratively with Multiple Language Models. CoRR abs/2403.03870 (2024) - [i84]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 abs/2404.02806 (2024) - [i83]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 abs/2404.15187 (2024) - [i82]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Theoretical Analysis of Weak-to-Strong Generalization. CoRR abs/2405.16043 (2024) - [i81]Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag:
Prediction-powered Generalization of Causal Inferences. CoRR abs/2406.02873 (2024) - [i80]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 abs/2407.09642 (2024) - [i79]Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar:
Need Help? Designing Proactive AI Assistants for Programming. CoRR abs/2410.04596 (2024) - 2023
- [c95]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: 5549-5581 - [c94]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 - [c93]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: 10520-10545 - [c92]Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag:
Conformalized Unconditional Quantile Regression. AISTATS 2023: 10690-10702 - [c91]Christina X. Ji, Ahmed M. Alaa, David A. Sontag:
Large-Scale Study of Temporal Shift in Health Insurance Claims. CHIL 2023: 243-278 - [c90]Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara D. 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: 343-359 - [c89]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 - [i78]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 abs/2301.06197 (2023) - [i77]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) - [i76]Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag:
Conformalized Unconditional Quantile Regression. CoRR abs/2304.01426 (2023) - [i75]Zejiang Shen, Tal August, Pao Siangliulue, Kyle Lo, Jonathan Bragg, Jeff Hammerbacher, Doug Downey, Joseph Chee Chang, David A. Sontag:
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks. CoRR abs/2304.02623 (2023) - [i74]Christina X. Ji, Ahmed M. Alaa, David A. Sontag:
Large-Scale Study of Temporal Shift in Health Insurance Claims. CoRR abs/2305.05087 (2023) - [i73]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 abs/2305.17261 (2023) - [i72]Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara D. Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag:
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. CoRR abs/2308.08494 (2023) - [i71]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 abs/2311.01007 (2023) - [i70]Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha, Bernhard Gapp, David A. Sontag, Yoon Kim:
Towards Verifiable Text Generation with Symbolic References. CoRR abs/2311.09188 (2023) - 2022
- [j8]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: 166:1-166:50 (2022) - [c88]Hussein Mozannar, Arvind Satyanarayan, David A. Sontag:
Teaching Humans When to Defer to a Classifier via Exemplars. AAAI 2022: 5323-5331 - [c87]Irene Y. Chen, Rahul G. Krishnan, David A. Sontag:
Clustering Interval-Censored Time-Series for Disease Phenotyping. AAAI 2022: 6211-6221 - [c86]Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag:
Leveraging Time Irreversibility with Order-Contrastive Pre-training. AISTATS 2022: 2330-2353 - [c85]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: 5459-5484 - [c84]Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag:
Large language models are few-shot clinical information extractors. EMNLP 2022: 1998-2022 - [c83]Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi:
Sample Efficient Learning of Predictors that Complement Humans. ICML 2022: 2972-3005 - [c82]Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag:
Co-training Improves Prompt-based Learning for Large Language Models. ICML 2022: 11985-12003 - [c81]Ahmed M. Alaa, Anthony Philippakis, David A. Sontag:
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. NeurIPS 2022 - [c80]Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. NeurIPS 2022 - [c79]Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag:
Training Subset Selection for Weak Supervision. NeurIPS 2022 - [c78]Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. NeurIPS 2022 - [i69]Hunter Lang, Monica Agrawal, Yoon Kim, David A. Sontag:
Co-training Improves Prompt-based Learning for Large Language Models. CoRR abs/2202.00828 (2022) - [i68]Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag:
Large Language Models are Zero-Shot Clinical Information Extractors. CoRR abs/2205.12689 (2022) - [i67]Nikolaj Thams, Michael Oberst, David A. Sontag:
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. CoRR abs/2205.15947 (2022) - [i66]Hunter Lang, Aravindan Vijayaraghavan, David A. Sontag:
Training Subset Selection for Weak Supervision. CoRR abs/2206.02914 (2022) - [i65]Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi:
Sample Efficient Learning of Predictors that Complement Humans. CoRR abs/2207.09584 (2022) - [i64]Zeshan M. Hussain, Michael Oberst, Ming-Chieh Shih, David A. Sontag:
Falsification before Extrapolation in Causal Effect Estimation. CoRR abs/2209.13708 (2022) - [i63]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 abs/2210.10723 (2022) - 2021
- [j7]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: 103844 (2021) - [c77]Rohan S. Kodialam, Rebecca Boiarsky, Justin Lim, Aditya Sai, Neil Dixit, David A. Sontag:
Deep Contextual Clinical Prediction with Reverse Distillation. AAAI 2021: 249-258 - [c76]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: 1365-1378 - [c75]Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka:
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS 2021: 1927-1935 - [c74]Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan:
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. AISTATS 2021: 3043-3051 - [c73]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: 72:1-72:13 - [c72]Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag:
Neural Pharmacodynamic State Space Modeling. ICML 2021: 4500-4510 - [c71]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). ICML 2021: 5990-5999 - [c70]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. ICML 2021: 8260-8270 - [c69]Jason Zhao, Monica Agrawal, Pedram Razavi, David A. Sontag:
Directing Human Attention in Event Localization for Clinical Timeline Creation. MLHC 2021: 80-102 - [c68]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 - [c67]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: 1169-1183 - [i62]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 abs/2102.06836 (2021) - [i61]Irene Y. Chen, Rahul G. Krishnan, David A. Sontag:
Clustering Left-Censored Multivariate Time-Series. CoRR abs/2102.07005 (2021) - [i60]Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag:
Neural Pharmacodynamic State Space Modeling. CoRR abs/2102.11218 (2021) - [i59]Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan:
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. CoRR abs/2103.00034 (2021) - [i58]Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies. CoRR abs/2103.02477 (2021) - [i57]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 abs/2103.04725 (2021) - [i56]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 abs/2106.02524 (2021) - [i55]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 abs/2109.11451 (2021) - [i54]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) - [i53]Rickard Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik D. Johansson:
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. CoRR abs/2110.14993 (2021) - [i52]Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag:
Leveraging Time Irreversibility with Order-Contrastive Pre-training. CoRR abs/2111.02599 (2021) - [i51]Hussein Mozannar, Arvind Satyanarayan, David A. Sontag:
Teaching Humans When To Defer to a Classifier via Examplars. CoRR abs/2111.11297 (2021) - 2020
- [j6]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): 147-153 (2020) - [c66]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 - [c65]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: 1211-1219 - [c64]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Bounds on Potential Outcomes For Decision Making. ICML 2020: 6661-6671 - [c63]Hussein Mozannar, David A. Sontag:
Consistent Estimators for Learning to Defer to an Expert. ICML 2020: 7076-7087 - [c62]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 - [c61]James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag:
Knowledge Base Completion for Constructing Problem-Oriented Medical Records. MLHC 2020: 198-222 - [c60]Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag:
Fast, Structured Clinical Documentation via Contextual Autocomplete. MLHC 2020: 842-870 - [c59]Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag:
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. MLHC 2020: 928-949 - [c58]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: 19-30 - [i50]Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag:
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR abs/2001.07426 (2020) - [i49]James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag:
Knowledge Base Completion for Constructing Problem-Oriented Medical Records. CoRR abs/2004.12905 (2020) - [i48]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) - [i47]Hussein Mozannar, David A. Sontag:
Consistent Estimators for Learning to Defer to an Expert. CoRR abs/2006.01862 (2020) - [i46]Rohan S. Kodialam, Rebecca Boiarsky, David A. Sontag:
Deep Contextual Clinical Prediction with Reverse Distillation. CoRR abs/2007.05611 (2020) - [i45]Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka:
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR abs/2007.11838 (2020) - [i44]Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag:
Fast, Structured Clinical Documentation via Contextual Autocomplete. CoRR abs/2007.15153 (2020) - [i43]Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag:
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. CoRR abs/2007.16127 (2020) - [i42]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) - [i41]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Graph cuts always find a global optimum (with a catch). CoRR abs/2011.03639 (2020)
2010 – 2019
- 2019
- [j5]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) - [j4]Ofer Meshi, Ben London, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. J. Mach. Learn. Res. 20: 13:1-13:34 (2019) - [c57]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Block Stability for MAP Inference. AISTATS 2019: 216-225 - [c56]Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath:
Support and Invertibility in Domain-Invariant Representations. AISTATS 2019: 527-536 - [c55]Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag:
Overcomplete Independent Component Analysis via SDP. AISTATS 2019: 2583-2592 - [c54]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. ICML 2019: 4881-4890 - [c53]Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chaplain, David A. Sontag, Xavier Amatriain:
Few-Shot Learning for Dermatological Disease Diagnosis. MLHC 2019: 532-552 - [i40]Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag:
Overcomplete Independent Component Analysis via SDP. CoRR abs/1901.08334 (2019) - [i39]Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath:
Support and Invertibility in Domain-Invariant Representations. CoRR abs/1903.03448 (2019) - [i38]Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. CoRR abs/1905.05824 (2019) - [i37]Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David A. Sontag:
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models. CoRR abs/1907.00030 (2019) - [i36]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) - [i35]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 abs/1910.01116 (2019) - [i34]Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David A. Sontag, Xavier Amatriain:
Open Set Medical Diagnosis. CoRR abs/1910.02830 (2019) - [i33]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR abs/1910.04817 (2019) - 2018
- [j3]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
Learning topic models - provably and efficiently. Commun. ACM 61(4): 85-93 (2018) - [c52]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Optimality of Approximate Inference Algorithms on Stable Instances. AISTATS 2018: 1157-1166 - [c51]Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush:
Semi-Amortized Variational Autoencoders. ICML 2018: 2683-2692 - [c50]Irene Y. Chen, Fredrik D. Johansson, David A. Sontag:
Why Is My Classifier Discriminatory? NeurIPS 2018: 3543-3554 - [c49]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:
Cell-specific prediction and application of drug-induced gene expression . PSB 2018: 32-43 - [c48]Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag:
Max-margin learning with the Bayes factor. UAI 2018: 896-905 - [i32]Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush:
Semi-Amortized Variational Autoencoders. CoRR abs/1802.02550 (2018) - [i31]Irene Y. Chen, Fredrik D. Johansson, David A. Sontag:
Why Is My Classifier Discriminatory? CoRR abs/1805.12002 (2018) - [i30]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:
Evaluating Reinforcement Learning Algorithms in Observational Health Settings. CoRR abs/1805.12298 (2018) - [i29]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Block Stability for MAP Inference. CoRR abs/1810.05305 (2018) - [i28]Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David A. Sontag, Xavier Amatriain:
Prototypical Clustering Networks for Dermatological Disease Diagnosis. CoRR abs/1811.03066 (2018) - 2017
- [c47]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. AAAI 2017: 2101-2109 - [c46]Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David A. Sontag, Rosalind W. Picard:
Objective assessment of depressive symptoms with machine learning and wearable sensors data. ACII 2017: 325-332 - [c45]Juan M. Banda, Yoni Halpern, David A. Sontag, Nigam Shah:
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. CRI 2017 - [c44]Yacine Jernite, Anna Choromanska, David A. Sontag:
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. ICML 2017: 1665-1674 - [c43]Uri Shalit, Fredrik D. Johansson, David A. Sontag:
Estimating individual treatment effect: generalization bounds and algorithms. ICML 2017: 3076-3085 - [c42]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017: 6446-6456 - [i27]Yacine Jernite, Samuel R. Bowman, David A. Sontag:
Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning. CoRR abs/1705.00557 (2017) - [i26]Ankit Vani, Yacine Jernite, David A. Sontag:
Grounded Recurrent Neural Networks. CoRR abs/1705.08557 (2017) - [i25]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - [i24]Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan:
Alpha-expansion is Exact on Stable Instances. CoRR abs/1711.02195 (2017) - 2016
- [j2]Yoni Halpern, Steven Horng, Youngduck Choi, David A. Sontag:
Electronic medical record phenotyping using the anchor and learn framework. J. Am. Medical Informatics Assoc. 23(4): 731-740 (2016) - [c41]Yoon Kim, Yacine Jernite, David A. Sontag, Alexander M. Rush:
Character-Aware Neural Language Models. AAAI 2016: 2741-2749 - [c40]Adrian Weller, Mark Rowland, David A. Sontag:
Tightness of LP Relaxations for Almost Balanced Models. AISTATS 2016: 47-55 - [c39]Fei Wang, Gregor Stiglic, Mihaela van der Schaar, David A. Sontag, Christopher C. Yang:
Data Mining for Medical Informatics (DMMI) - Learning Health. AMIA 2016 - [c38]Saul Blecker, Stuart Katz, Leora Horwitz, Gilad J. Kuperman, Hannah Park, David A. Sontag:
Comparison of Approaches for Heart Failure Case Identification from EHR Data. AMIA 2016 - [c37]Youngduck Choi, Chill Yi-I Chiu, David A. Sontag:
Learning Low-Dimensional Representations of Medical Concepts. CRI 2016 - [c36]Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag:
Train and Test Tightness of LP Relaxations in Structured Prediction. ICML 2016: 1776-1785 - [c35]Fredrik D. Johansson, Uri Shalit, David A. Sontag:
Learning Representations for Counterfactual Inference. ICML 2016: 3020-3029 - [c34]Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh:
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. MLHC 2016: 17-41 - [c33]Narges Razavian, Jake Marcus, David A. Sontag:
Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. MLHC 2016: 73-100 - [c32]Yoni Halpern, Steven Horng, David A. Sontag:
Clinical Tagging with Joint Probabilistic Models. MLHC 2016: 209-225 - [i23]Fredrik D. Johansson, Uri Shalit, David A. Sontag:
Learning Representations for Counterfactual Inference. CoRR abs/1605.03661 (2016) - [i22]Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David A. Sontag, Yan Liu:
Recurrent Neural Networks for Multivariate Time Series with Missing Values. CoRR abs/1606.01865 (2016) - [i21]Uri Shalit, Fredrik D. Johansson, David A. Sontag:
Bounding and Minimizing Counterfactual Error. CoRR abs/1606.03976 (2016) - [i20]Narges Razavian, Jake Marcus, David A. Sontag:
Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests. CoRR abs/1608.00647 (2016) - [i19]Yoni Halpern, Steven Horng, David A. Sontag:
Clinical Tagging with Joint Probabilistic Models. CoRR abs/1608.00686 (2016) - [i18]Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh:
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. CoRR abs/1608.00704 (2016) - [i17]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Structured Inference Networks for Nonlinear State Space Models. CoRR abs/1609.09869 (2016) - [i16]Yacine Jernite, Anna Choromanska, David A. Sontag, Yann LeCun:
Simultaneous Learning of Trees and Representations for Extreme Classification, with Application to Language Modeling. CoRR abs/1610.04658 (2016) - 2015
- [j1]Narges Razavian, Saul Blecker, Ann Marie Schmidt, Aaron Smith-McLallen, Somesh Nigam, David A. Sontag:
Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors. Big Data 3(4): 277-287 (2015) - [c31]Josua Krause, Narges Razavian, Enrico Bertini, David A. Sontag:
Visual Exploration of Temporal Data in Electronic Medical Records. AMIA 2015 - [c30]Narges Razavian, Saul Blecker, David A. Sontag:
Gaussian Processes for interpreting Multiple Prostate Specific Antigen measurements for Prostate Cancer Prediction. AMIA 2015 - [c29]Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim:
How Hard is Inference for Structured Prediction? ICML 2015: 2181-2190 - [c28]Yacine Jernite, Alexander M. Rush, David A. Sontag:
A Fast Variational Approach for Learning Markov Random Field Language Models. ICML 2015: 2209-2217 - [c27]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. NIPS 2015: 532-540 - [i15]Eliot Brenner, David A. Sontag:
Incorporating Type II Error Probabilities from Independence Tests into Score-Based Learning of Bayesian Network Structure. CoRR abs/1505.02870 (2015) - [i14]Yoon Kim, Yacine Jernite, David A. Sontag, Alexander M. Rush:
Character-Aware Neural Language Models. CoRR abs/1508.06615 (2015) - [i13]Ofer Meshi, Mehrdad Mahdavi, David A. Sontag:
On the Tightness of LP Relaxations for Structured Prediction. CoRR abs/1511.01419 (2015) - [i12]Rahul G. Krishnan, Simon Lacoste-Julien, David A. Sontag:
Barrier Frank-Wolfe for Marginal Inference. CoRR abs/1511.02124 (2015) - [i11]Yoni Halpern, Steven Horng, David A. Sontag:
Anchored Discrete Factor Analysis. CoRR abs/1511.03299 (2015) - [i10]Rahul G. Krishnan, Uri Shalit, David A. Sontag:
Deep Kalman Filters. CoRR abs/1511.05121 (2015) - [i9]Narges Razavian, David A. Sontag:
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests. CoRR abs/1511.07938 (2015) - 2014
- [c26]Yoni Halpern, Youngduck Choi, Steven Horng, David A. Sontag:
Using Anchors to Estimate Clinical State without Labeled Data. AMIA 2014 - [c25]Nathan Silberman, David A. Sontag, Rob Fergus:
Instance Segmentation of Indoor Scenes Using a Coverage Loss. ECCV (1) 2014: 616-631 - [c24]Xiang Wang, David A. Sontag, Fei Wang:
Unsupervised learning of disease progression models. KDD 2014: 85-94 - [c23]Hung Hai Bui, Tuyen N. Huynh, David A. Sontag:
Lifted Tree-Reweighted Variational Inference. UAI 2014: 92-101 - [c22]Adrian Weller, Kui Tang, Tony Jebara, David A. Sontag:
Understanding the Bethe Approximation: When and How can it go Wrong? UAI 2014: 868-877 - [i8]Hung Hai Bui, Tuyen N. Huynh, David A. Sontag:
Lifted Tree-Reweighted Variational Inference. CoRR abs/1406.4200 (2014) - [i7]Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim:
Tight Error Bounds for Structured Prediction. CoRR abs/1409.5834 (2014) - 2013
- [c21]Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) 2013: 280-288 - [c20]Yacine Jernite, Yonatan Halpern, David A. Sontag:
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. NIPS 2013: 2355-2363 - [c19]Eliot Brenner, David A. Sontag:
SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. UAI 2013 - [c18]Yonatan Halpern, David A. Sontag:
Unsupervised Learning of Noisy-Or Bayesian Networks. UAI 2013 - [i6]Eliot Brenner, David A. Sontag:
SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. CoRR abs/1309.6820 (2013) - [i5]Yonatan Halpern, David A. Sontag:
Unsupervised Learning of Noisy-Or Bayesian Networks. CoRR abs/1309.6834 (2013) - 2012
- [c17]David A. Sontag, Do Kook Choe, Yitao Li:
Efficiently Searching for Frustrated Cycles in MAP Inference. UAI 2012: 795-804 - [c16]David A. Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan T. Dumais, Bodo Billerbeck:
Probabilistic models for personalizing web search. WSDM 2012: 433-442 - [i4]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. CoRR abs/1206.3288 (2012) - [i3]David A. Sontag, Do Kook Choe, Yitao Li:
Efficiently Searching for Frustrated Cycles in MAP Inference. CoRR abs/1210.4902 (2012) - [i2]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. CoRR abs/1212.4777 (2012) - 2011
- [c15]Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David A. Sontag:
Personalizing web search results by reading level. CIKM 2011: 403-412 - [c14]David A. Sontag, Daniel M. Roy:
Complexity of Inference in Latent Dirichlet Allocation. NIPS 2011: 1008-1016 - 2010
- [b1]David A. Sontag:
Approximate inference in graphical models using linear programming relaxations. Massachusetts Institute of Technology, Cambridge, MA, USA, 2010 - [c13]Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola:
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. EMNLP 2010: 1-11 - [c12]Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag:
Dual Decomposition for Parsing with Non-Projective Head Automata. EMNLP 2010: 1288-1298 - [c11]Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson:
Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790 - [c10]David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189 - [c9]Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila:
Learning Bayesian Network Structure using LP Relaxations. AISTATS 2010: 358-365
2000 – 2009
- 2009
- [c8]David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger:
Scaling all-pairs overlay routing. CoNEXT 2009: 145-156 - [c7]David A. Sontag, Tommi S. Jaakkola:
Tree Block Coordinate Descent for MAP in Graphical Models. AISTATS 2009: 544-551 - 2008
- [c6]David A. Sontag, Amir Globerson, Tommi S. Jaakkola:
Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544 - [c5]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510 - 2007
- [c4]David A. Sontag, Tommi S. Jaakkola:
New Outer Bounds on the Marginal Polytope. NIPS 2007: 1393-1400 - [c3]David A. Sontag, Rohit Singh, Bonnie Berger:
Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments. Pacific Symposium on Biocomputing 2007: 445-457 - 2005
- [c2]Brian Milch, Bhaskara Marthi, David A. Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov:
Approximate Inference for Infinite Contingent Bayesian Networks. AISTATS 2005: 238-245 - [c1]Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov:
BLOG: Probabilistic Models with Unknown Objects. IJCAI 2005: 1352-1359 - [i1]Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov:
BLOG: Probabilistic Models with Unknown Objects. Probabilistic, Logical and Relational Learning 2005
Coauthor Index
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