Publications

Lucas Torroba Hennigen, Shannon Shen, Aniruddha Nrusimha, Bernhard Gapp, David Sontag, Yoon Kim (2024). Towards Verifiable Text Generation with Symbolic References. COLM 2024.

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Shannon Shen, Hunter Lang, Bailin Wang, Yoon Kim, David Sontag (2024). Learning to Decode Collaboratively with Multiple Language Models. ACL 2024.

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Ilker Demirel, Ahmed Alaa, Anthony Philippakis, David Sontag (2024). Prediction-powered Generalization of Causal Inferences. ICML 2024.

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Stefan Hegselmann, Shannon Shen, Florian Gierse, Monica Agrawal, David Sontag, Xiaoyi Jiang (2024). A Data-Centric Approach to Generate Faithful and High Quality Patient Summaries with Large Language Models. Conference on Health, Inference, and Learning (CHIL) 2024.

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Ilker Demirel, Edward De Brouwer, Zeshan Hussain, Michael Oberst, Anthony A Philippakis, David Sontag (2024). Benchmarking observational studies with experimental data under right-censoring. AISTATS 2024.

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Sharon Jiang, Barbara D Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R Karger, David Sontag (2024). Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing. Journal of the American Medical Informatics Association (JAMIA).

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Zeshan Hussain, Edward De Brouwer, Rebecca Boiarsky, Sama Setty, Neeraj Gupta, Guohui Liu, Cong Li, Jaydeep Srimani, Jacob Zhang, Rich Labotka, others (2024). Joint AI-driven event prediction and longitudinal modeling in newly diagnosed and relapsed multiple myeloma. NPJ Digital Medicine.

Hussein Mozannar, Jimin J Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag (2023). Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. Advances in neural information processing systems (NeurIPS).

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Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David Karger, David Sontag (2023). Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. Machine Learning for Healthcare Conference (MLHC), 2023.

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Christina X Ji, Ahmed M Alaa, David Sontag (2023). Large-Scale Study of Temporal Shift in Health Insurance Claims. Conference on Health, Inference, and Learning.

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Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag (2023). Who Should Predict? Exact Algorithms For Learning to Defer to Humans. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David Sontag (2023). TabLLM: Few-shot Classification of Tabular Data with Large Language Models. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag (2023). Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Ahmed Alaa, Zeshan Hussain, David Sontag (2023). Conformalized Unconditional Quantile Regression. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

Rebecca Boiarsky, Nalini M. Singh, Alejandro Buendia, Gad Getz, David Sontag (2023). A Deep Dive into Single-Cell RNA Sequencing Foundation Models. bioRxiv.

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Hunter Lang, Aravindan Vijayaraghavan, David Sontag (2022). Training Subset Selection for Weak Supervision. Advances in Neural Information Processing Systems.

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Zeshan Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag (2022). Falsification before Extrapolation in Causal Effect Estimation. Advances in Neural Information Processing Systems (NeurIPS).

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Nikolaj Thams, Michael Oberst, David Sontag (2022). Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. Advances in Neural Information Processing Systems (NeurIPS).

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Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi (2022). Sample Efficient Learning of Predictors that Complement Humans. Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML).

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Hunter Lang, Monica Agrawal, Yoon Kim, David Sontag (2022). Co-training Improves Prompt-based Learning for Large Language Models. Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML).

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Michael Oberst, Alexander D'Amour, Minmin Chen, Yuyan Wang, David Sontag, Steve Yadlowsky (2022). Bias-robust Integration of Observational and Experimental Estimators. arXiv preprint (2205.10467).

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Rickard Karlsson, Martin Willbo, Zeshan Hussain, Rahul G Krishnan, David Sontag, Fredrik D Johansson (2022). Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Stephanie S Gervasi, Irene Chen, Aaron Smith-McLallen, David Sontag, Ziad Obermeyer, Michael Vennera, Ravi Chawla (2022). The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It. Health Affairs.

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Hussein Mozannar, Arvind Satyanarayan, David Sontag (2022). Teaching Humans When To Defer to a Classifier via Exemplars. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI).

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Rebecca Boiarsky, Nicholas J Haradhvala, Jean-Baptiste Alberge, Romanos Sklavenitis-Pistofidis, Tarek H Mouhieddine, Oksana Zavidij, Ming-Chieh Shih, Danielle Firer, Mendy Miller, Habib El-Khoury, Shankara K Anand, Francois Aguet, David Sontag, Irene M Ghobrial, Gad Getz (2022). Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nature Communications.

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Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David Sontag (2022). Leveraging Time Irreversibility with Order-Contrastive Pre-training. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David Sontag (2022). Large Language Models are Few-Shot Clinical Information Extractors. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

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Ahmed Alaa, Anthony Philippakis, David Sontag (2022). ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography. Advances in Neural Information Processing Systems Datasets and Benchmarks Track.

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Irene Chen, Rahul Krishnan, David Sontag (2022). Clustering Interval-Censored Time-Series for Disease Phenotyping. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI).

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Luke Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David Sontag, David Karger (2021). MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. UIST ‘21: The 34th Annual ACM Symposium on User Interface Software and Technology.

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Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David Sontag (2021). Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance. Proceedings of the 35th Conference on Neural Information Processing Systems.

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Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag (2021). Regularizing towards Causal Invariance: Linear Models with Proxies. Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML).

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Zeshan Hussain, Rahul G Krishnan, David Sontag (2021). Neural Pharmacodynamic State Space Modeling. Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML).

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Hunter Lang, David Sontag, Aravindan Vijayaraghavan (2021). Graph cuts always find a global optimum for Potts models (with a catch). Proceedings of the Thirty-Eighth International Conference on Machine Learning (ICML).

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Christina X Ji, Michael Oberst, Sanjat Kanjilal, David Sontag (2021). Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies. AMIA 2021 Virtual Informatics Summit.

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Matthew Alkaitis, Monica Agrawal, Gregory Riely, Pedram Razavi, David Sontag (2021). Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer. JCO Clinical Cancer Informatics.

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Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David Sontag (2021). Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CHI Conference on Human Factors in Computing Systems.

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Alexander Lew, Monica Agrawal, David Sontag, Vikash Mansinghka (2021). PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS).

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Hunter Lang, Aravind Reddy, David Sontag, Aravindan Vijayaraghavan (2021). Beyond perturbation stability: LP recovery guarantees for MAP inference on noisy stable instances. Proceedings of the Twenty-Fourth International Conference on Artificial Intelligence and Statistics (AISTATS).

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Julia Wu, Venkatesh Sivaraman, Dheekshita Kumar, Juan M. Banda, David Sontag (2021). Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media. Journal of Biomedical Informatics.

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Justin Lim (2021). MEng Thesis: Identifying Heterogeneity in Decision-Making.

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Jason Zhao, Monica Agrawal, Pedram Razavi, David Sontag (2021). Directing Human Attention in Event Localization for Clinical Timeline Creation. Proceedings of the Machine Learning for Healthcare Conference.

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Rohan Kodialam, Rebecca Boiarsky, Justin Lim, Neil Dixit, Aditya Sai, David Sontag (2021). Deep Contextual Clinical Prediction with Reverse Distillation. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence.

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Sooraj Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David Sontag (2020). Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

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Irene Chen, Monica Agrawal, Steven Horng, David Sontag (2020). Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph. Proceedings of the Pacific Symposium on Biocomputing (PSB).

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Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David Sontag (2020). Robust Benchmarking for Machine Learning of Clinical Entity Extraction. Proceedings of the Machine Learning for Healthcare Conference.

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Jamie E. Collins, Fredrik D. Johansson, Sara Gale, Seoyoung Kim, Swastina Shrestha, David Sontag, Jacklyn Stratton, Huong Trinh, Chang Xu, Elena Losina, Daniel H. Solomon (2020). Predicting Remission Among Patients With Rheumatoid Arthritis Starting Tocilizumab Monotherapy: Model Derivation and Remission Score Development. ACR Open Rheumatology.

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Ethan D Evans, Claire Duvallet, Nathaniel D Chu, Michael K Oberst, Michael A Murphy, Isaac Rockafellow, David Sontag, Eric J Alm (2020). Predicting human health from biofluid-based metabolomics using machine learning. Scientific Reports.

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Rahul Krishnan (2020). PhD Thesis: Advances in deep generative modeling for clinical data.

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Rohan Kodialam (2020). MEng Thesis: Pipelines for Deep Patient-Level Clinical Outcome Prediction.

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Divya Gopinath (2020). MEng Thesis: ML-Driven Clinical Documentation.

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Sooraj Boominathan (2020). MEng Thesis: Learning Treatment Policies for Empiric Antibiotic Prescription.

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Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag (2020). Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. arXiv preprint arXiv:2001.07426.

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Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag (2020). Fast, Structured Clinical Documentation via Contextual Autocomplete. Proceedings of the Machine Learning for Healthcare Conference.

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Maggie Makar, Fredrik Johansson, John Guttag, David Sontag (2020). Estimation of Bounds on Potential Outcomes For Decision Making. Proceedings of the Thirty-Seventh International Conference on Machine Learning (ICML).

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Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag (2020). Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. Proceedings of the Thirty-Seventh International Conference on Machine Learning (ICML).

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Hussein Mozannar, David Sontag (2020). Consistent Estimators for Learning to Defer to an Expert. Proceedings of the Thirty-Seventh International Conference on Machine Learning (ICML).

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Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush R. Varshney (2020). Characterization of Overlap in Observational Studies. Proceedings of the Twenty-Third International Conference on Artificial Intelligence and Statistics (AISTATS).

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Sanjat Kanjilal, Michael Oberst, Sooraj Boominathan, Helen Zhou, David C. Hooper, David Sontag (2020). A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection. Science Translational Medicine.

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Colby Redfield, Abdulhakim Tlimat, Yoni Halpern, David W Schoenfeld, Edward Ullman, David Sontag, Larry Nathanson, Steven Horng (2019). Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department. Journal of the American Medical Informatics Association.

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Ofer Meshi, Ben London, Adrian Weller, David Sontag (2019). Train and Test Tightness of LP Relaxations in Structured Prediction. Journal of Machine Learning Research.

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Fredrik D Johansson, Rajesh Ranganath, David Sontag (2019). Support and Invertibility in Domain-Invariant Representations. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS).

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Anastasia Podosinnikova, Amelia Perry, Alexander Wein, Francis Bach, Alexandre d'Aspremont, David Sontag (2019). Overcomplete Independent Component Analysis via SDP. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS).

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Christina X Ji (2019). MEng Thesis: Modeling progression of Parkinson's disease.

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Arjun Khandwelwal (2019). MEng Thesis: Fine-tuning Generative Models.

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Michael Oberst (2019). Master's Thesis: Counterfactual Policy Introspection using Structural Causal Models.

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Nathaniel R. Greenbaum, Yacine Jernite, Yoni Halpern, Shelley Calder, Larry A. Nathanson, David A. Sontag, Steven Horng (2019). Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces. International Journal of Medical Informatics.

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O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, F Doshi-Velez, LA Celi (2019). Guidelines for reinforcement learning in healthcare.. Nature medicine.

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Michael Oberst, David Sontag (2019). Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. Proceedings of the Thirty-Sixth International Conference on Machine Learning (ICML).

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Hunter Lang, David Sontag, Aravindan Vijayaraghavan (2019). Block Stability for MAP Inference. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS).

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Irene Chen, Fredrik D. Johansson, David Sontag (2018). Why Is My Classifier Discriminatory?. Proceedings of the 32nd International Conference on Neural Information Processing Systems.

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Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush (2018). Semi-Amortized Variational Autoencoders. Proceedings of the 35th International Conference on Machine Learning (ICML).

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Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, Yan Liu (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Nature Scientific Reports.

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Yacine Jernite (2018). PhD Thesis: Learning Representations of Text through Language and Discourse Modeling: From Characters to Sentences.

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Rachel Hodos (2018). PhD Thesis: Filling Gaps and Dodging Traps: Leveraging untapped potential in gene expression data for drug discovery with applications to cystic fibrosis.

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Hunter Lang, David Sontag, Aravindan Vijayaraghavan (2018). Optimality of Approximate Inference Algorithms on Stable Instances. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AI-STATS).

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Hunter Lang (2018). MEng Thesis: Perturbation Stability for Approximate MAP Inference.

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Helen Zhou (2018). MEng Thesis: Large-scale Prediction of Patient-Level Antibiotic Resistance: Towards Clinical Decision Support for Improved Antimicrobial Stewardship.

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Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag (2018). Max-margin learning with the Bayes Factor. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

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Fredrik D Johansson (2018). Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions. arXiv preprint arXiv:1811.05975.

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Fredrik D. Johansson, Nathan Kallus, Uri Shalit, David Sontag (2018). Learning Weighted Representations for Generalization Across Designs. ArXiv e-prints arXiv:1802.08598.

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Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu (2018). Learning Topic Models - Provably and Efficiently. Communications of the ACM.

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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 Sontag, Finale Doshi-Velez (2018). Evaluating Reinforcement Learning Algorithms in Observational Health Settings.

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Rachel Hodos, Ping Zhang, Hao Chih Lee, Qiaonan Duan, Zichen Wang, Neil R. Clark, Avi Ma'ayan, Fei Wang, Brian Kidd, Jianying Hu, David Sontag, Joel Dudley (2018). Cell-specific prediction and application of drug-induced gene expression profiles. Proceedings of the Pacific Symposium on Biocomputing (PSB).

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Anasuya Das, Leifur Thorbergsson, Aleksandr Grigorenko, David Sontag, Iker Huerga (2017). Using Machine Learning to Recommend Oncology Clinical Trials. Machine Learning for Health Care (Clinical abstract).

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Rahul G. Krishnan, Uri Shalit, David Sontag (2017). Structured Inference Networks for Nonlinear State Space Models. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.

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Yacine Jernite, Anna Choromanska, David Sontag (2017). Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. Proceedings of the 34th International Conference on Machine Learning.

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Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David Sontag, Rosalind Picard (2017). Objective Assessment of Depressive Symptoms with Machine Learning and Wearable Sensors Data. Proceedings of the Seventh International Conference on Affective Computing and Intelligent Interaction (ACII).

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Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, David Sontag (2017). Learning a Health Knowledge Graph from Electronic Medical Records. Nature Scientific Reports.

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A. Vani, Y. Jernite, D. Sontag (2017). Grounded Recurrent Neural Networks. ArXiv e-prints arXiv:1705.08557.

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Uri Shalit, Fredrik D. Johansson, David Sontag (2017). Estimating individual treatment effect: generalization bounds and algorithms. Proceedings of the 34th International Conference on Machine Learning.

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Juan M. Banda, Yoni Halpern, David Sontag, Nigam H. Shah (2017). Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. Proceedings of the AMIA Summit on Clinical Research Informatics (CRI).

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Saul Blecker, David Sontag, Leora Horwitz, Gilad Kuperman, Hannah Park, Alex Reyentovich, Stuart Katz (2017). Early Identification of Patients with Acute Decompensated Heart Failure. Journal of Cardiac Failure.

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Yacine Jernite, Samuel R Bowman, David Sontag (2017). Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning. arXiv preprint arXiv:1705.00557.

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Steven Horng, David Sontag, Yoni Halpern, Yacine Jernite, Nathan I. Shapiro, Larry A. Nathanson (2017). Creating an Automated Trigger for Sepsis Clinical Decision Support at Emergency Department Triage using Machine Learning. PLoS ONE.

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Nathaniel R Greenbaum, Yacine Jernite, Yoni Halpern, Shelley Calder, Larry A. Nathanson, David Sontag, Steven Horng (2017). Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department. bioRxiv.

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Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard S. Zemel, Max Welling (2017). Causal Effect Inference with Deep Latent-Variable Models. Proceedings of the 31st International Conference on Neural Information Processing Systems.

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Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag (2016). Train and Test Tightness of LP Relaxations in Structured Prediction. Proceedings of The 33rd International Conference on Machine Learning.

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Adrian Weller, Mark Rowland, David Sontag (2016). Tightness of LP Relaxations for Almost Balanced Models. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics.

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Narges Razavian, Saul Blecker, Ann Marie Schmidt, Aaron Smith-McLallen, Somesh Nigam, David Sontag (2016). Population-Level Prediction of Type 2 Diabetes using Claims Data and Analysis of Risk Factors. Big Data.

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Yoni Halpern (2016). PhD Thesis: Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine.

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Narges Razavian, Jake Marcus, David Sontag (2016). Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. Proceedings of the 1st Machine Learning for Healthcare Conference.

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Fredrik Johansson, Uri Shalit, David Sontag (2016). Learning Representations for Counterfactual Inference. Proceedings of The 33rd International Conference on Machine Learning.

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Youngduck Choi, Yi-I Chiu, David Sontag (2016). Learning Low-Dimensional Representations of Medical Concepts. Proceedings of the AMIA Summit on Clinical Research Informatics (CRI).

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Shalmali Joshi, Suriya Gunasekar, David Sontag, Ghosh Joydeep (2016). Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. Proceedings of the 1st Machine Learning for Healthcare Conference.

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Yoni Halpern, Steven Horng, Youngduck Choi, David Sontag (2016). Electronic Medical Record Phenotyping using the Anchor & Learn Framework. Journal of the American Medical Informatics Association (JAMIA).

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Saul Blecker, Stuart Katz, LI Horwitz, Gilad Kuperman, H Park, A Gold, David Sontag (2016). Comparison of approaches for heart failure case identification from electronic health record data. JAMA Cardiology.

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Yoni Halpern, Steven Horng, David Sontag (2016). Clinical Tagging with Joint Probabilistic Models. Proceedings of the 1st Machine Learning for Healthcare Conference.

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Yoon Kim, Yacine Jernite, David Sontag, Alexander M. Rush (2016). Character-Aware Neural Language Models. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.

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Josua Krause, Narges Razavian, Enrico Bertini, David Sontag (2015). Visual Exploration of Temporal Data in Electronic Medical Records. Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium (Abstract).

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Narges Razavian, David Sontag (2015). Temporal Convolutional Neural Networks for Diagnosis from Lab Tests. arXiv preprint arXiv:1511.07938.

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N. Razavian, A. Smith-McLallen, S. Nigam, S. Blecker, A. M. Schmidt, D. Sontag (2015). Predicting chronic comorbid conditions of type 2 diabetes in Newly-Diagnosed Diabetic Patients. Value in Health (Abstract).

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Nathan Silberman (2015). PhD Thesis: Reasoning about Object Instances, Relations and Extents in RGBD Scenes.

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Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim (2015). How Hard is Inference for Structured Prediction?. Proceedings of the 32nd International Conference on Machine Learning (ICML).

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Rahul G. Krishnan, Uri Shalit, David Sontag (2015). Deep Kalman Filters. arXiv preprint arXiv:1511.05121.

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Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag (2015). Barrier Frank-Wolfe for Marginal Inference. Proceedings of the 28th International Conference on Neural Information Processing Systems.

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Yoni Halpern, Steven Horng, David Sontag (2015). Anchored Discrete Factor Analysis. arXiv preprint arXiv:1511.03299.

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Yacine Jernite, Alexander Rush, David Sontag (2015). A Fast Variational Approach for Learning Markov Random Field Language Models. Proceedings of the 32nd International Conference on Machine Learning (ICML).

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Yoni Halpern, Youngduck Choi, Steven Horng, David Sontag (2014). Using Anchors to Estimate Clinical State without Labeled Data. Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium.

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Xiang Wang, David Sontag, Fei Wang (2014). Unsupervised Learning of Disease Progression Models. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

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Adrian Weller, Kui Tang, David Sontag, Tony Jebara (2014). Understanding the Bethe Approximation: When and How can it go Wrong?. Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI-14).

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Hung Hai Bui, Tuyen N. Huynh, David Sontag (2014). Lifted Tree-Reweighted Variational Inference. Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI-14).

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Nathan Silberman, David Sontag, Rob Fergus (2014). Instance Segmentation of Indoor Scenes using a Coverage Loss. Proceedings of the 13th European Conference on Computer Vision (ECCV).

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Yoni Halpern, David Sontag (2013). Unsupervised Learning of Noisy-Or Bayesian Networks. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI-13).

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Eliot Brenner, David Sontag (2013). SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI-13).

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Yacine Jernite, Yoni Halpern, Steven Horng, David Sontag (2013). Predicting Chief Complaints at Triage Time in the Emergency Department. NeurIPS Workshop on Machine Learning for Clinical Data Analysis and Healthcare.

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Yacine Jernite, Yoni Halpern, David Sontag (2013). Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. Advances in Neural Information Processing Systems 26.

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Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. Proceedings of the International Conference on Machine Learning (ICML).

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David Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan Dumais, Bodo Billerbeck (2012). Probabilistic models for personalizing web search. Proceedings of the Fifth ACM International Conference on Web Search and Data Mining.

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David Sontag, Amir Globerson, Tommi Jaakkola (2012). Introduction to Dual Decomposition for Inference. Optimization for Machine Learning.

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David Sontag, Do Kook Choe, Yitao Li (2012). Efficiently Searching for Frustrated Cycles in MAP Inference. Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI-12).

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Yoni Halpern, Steven Horng, Larry A. Nathanson, Nathan I. Shapiro, David Sontag (2012). A Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text. ICML 2012 Workshop on Clinical Data Analysis.

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Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David Sontag (2011). Personalizing web search results by reading level. Proceedings of the 20th ACM International Conference on Information and Knowledge Management.

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David Sontag, Dan Roy (2011). Complexity of Inference in Latent Dirichlet Allocation. Advances in Neural Information Processing Systems 24.

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David Sontag (2010). PhD Thesis: Approximate Inference in Graphical Models using LP Relaxations.

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Alexander M. Rush, David Sontag, Michael Collins, Tommi Jaakkola (2010). On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson (2010). More data means less inference: A pseudo-max approach to structured learning. Advances in Neural Information Processing Systems 23.

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Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson (2010). Learning Efficiently with Approximate Inference via Dual Losses. Proceedings of the 27th International Conference on Machine Learning (ICML).

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Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila (2010). Learning Bayesian Network Structure using LP Relaxations. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AI-STATS).

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Terry Koo, Alexander M. Rush, Michael Collins, Tommi Jaakkola, David Sontag (2010). Dual Decomposition for Parsing with Non-Projective Head Automata. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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David Sontag, Tommi Jaakkola (2009). Tree Block Coordinate Descent for MAP in Graphical Models. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AI-STATS).

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David Sontag, Yang Zhang, Amar Phanishayee, David Andersen, David Karger (2009). Scaling All-Pairs Overlay Routing. CoNEXT ‘09: Proceedings of the 5th international conference on Emerging networking experiments and technologies.

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David Sontag, Amir Globerson, Tommi Jaakkola (2009). Clusters and Coarse Partitions in LP Relaxations. Advances in Neural Information Processing Systems 21.

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David Sontag, Talya Meltzer, Amir Globerson, Yair Weiss, Tommi Jaakkola (2008). Tightening LP Relaxations for MAP using Message-Passing. 24th Conference on Uncertainty in Artificial Intelligence.

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David Sontag, Tommi Jaakkola (2008). New Outer Bounds on the Marginal Polytope. Advances in Neural Information Processing Systems 20.

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David Sontag, Rohit Singh, Bonnie Berger (2007). Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments. Pacific Symposium on Biocomputing.

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David Sontag (2007). Master's Thesis: Cutting Plane Algorithms for Variational Inference in Graphical Models.

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Brian Milch, Bhaskara Marthi, Stuart Russell, David Sontag, Daniel L. Ong, Andrey Kolobov (2005). BLOG: probabilistic models with unknown objects. IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligence.

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Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov (2005). Approximate Inference for Infinite Contingent Bayesian Networks. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics.

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