Please see here for updated publication list.
- A. Vani, Y. Jernite, D. Sontag. Grounded Recurrent Neural Networks. arXiv:1705.08557 Preprint, 2017.
- C. Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, M. Welling. Causal Effect Inference with Deep Latent-Variable Models. arXiv:1705.08821 Preprint, 2017.
- Y. Jernite, S. Bowman, D. Sontag. Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning. arXiv:1705.00557 Preprint, 2017.
- M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng,
D. Sontag. Learning a Health Knowledge Graph from Electronic Medical Records, Nature Scientific Reports, July 2017.
- U. Shalit, F. Johansson, D. Sontag. Estimating Individual Treatment Effect: Generalization Bounds and Algorithms. To appear in the 34rd International Conference on Machine Learning (ICML), 2017. [code] [Slides]
- Y. Jernite, A. Choromanska, D. Sontag. Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. To appear in the 34rd International Conference on Machine Learning (ICML), 2017.
- R. Krishnan, U. Shalit, D. Sontag. Structured Inference Networks for Nonlinear State Space Models, To appear in the Thirty-First AAAI Conference on Artificial Intelligence, Feb. 2017. [code] Older version
- F. Johansson, U. Shalit, D. Sontag. Learning Representations for Counterfactual Inference. 33rd International Conference on Machine Learning (ICML), June 2016. Supplementary [arXiv] [code] [Slides]
- O. Meshi, M. Mahdavi, A. Weller, D. Sontag. Train and Test Tightness of LP Relaxations in Structured Prediction. 33rd International Conference on Machine Learning (ICML), June 2016. [arXiv]
- Y. Halpern, S. Horng, D. Sontag. Clinical Tagging with Joint Probabilistic Models. 1st Conference on Machine Learning and Health Care (MLHC), Aug. 2016.
- N. Razavian, J. Marcus, D. Sontag. Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests. 1st Conference on Machine Learning and Health Care (MLHC), Aug. 2016. [ICLR workshop paper] [code]
- S. Joshi, S. Gunasekar, D. Sontag, J. Ghosh. Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. 1st Conference on Machine Learning and Health Care (MLHC), Aug. 2016.
- A. Weller, M. Rowland, D. Sontag. Tightness of LP Relaxations for Almost Balanced Models. 19th International Conference on Artificial Intelligence and Statistics (AISTATS), May 2016. Supplementary
- Y. Halpern, S. Horng, Y. Choi, D. Sontag. Electronic Medical Record Phenotyping using the Anchor and Learn Framework. Journal of the American Medical Informatics Association (JAMIA), April 2016. [html] [code]
- S. Blecker, S. Katz, L. Horwitz, G. Kuperman, H. Park, A. Gold, D. Sontag. Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data. Journal of the American Medical Association (JAMA) Cardiology, Oct. 2016.
- Y. Kim, Y. Jernite, D. Sontag, S. Rush. Character-Aware Neural Language Models, Thirtieth AAAI Conference on Artificial Intelligence, Feb. 2016. [code] [Slides] Video
- Z. Che, S. Purushotham, K. Cho, D. Sontag, Y. Liu. Recurrent Neural Networks for Multivariate Time Series with Missing Values, arXiv:1606.01865 Preprint, 2016.
- Y. Halpern, S. Horng, D. Sontag. Anchored Discrete Factor Analysis, arXiv:1511.03299 Preprint, 2015.
- Y. Choi, Y. Chiu, D. Sontag. Learning Low-Dimensional Representations of Medical Concepts. Proceedings of the AMIA Summit on Clinical Research Informatics (CRI), March 2016. [code]
- N. Razavian, S. Blecker, A.M. Schmidt, A. Smith-McLallen, S. Nigam, D. Sontag. Population-Level Prediction of Type 2 Diabetes using Claims Data and Analysis of Risk Factors. Big Data (Data and Healthcare Special Issue), Jan. 2016. Supplementary
- R. Krishnan, S. Lacoste-Julien, D. Sontag. Barrier Frank-Wolfe for Marginal Inference. Neural Information Processing Systems (NIPS) 28, Dec. 2015. [code]
- Y. Jernite, S. Rush, D. Sontag. A Fast Variational Approach for Learning Markov Random Field Language Models. 32nd International Conference on Machine Learning (ICML), July 2015. [Slides] [code]
- A. Globerson, T. Roughgarden, D. Sontag, C. Yildirim. How Hard is Inference for Structured Prediction? 32nd International Conference on Machine Learning (ICML), July 2015. arXiv [Slides]
- Y. Halpern, Y.D. Choi, S. Horng, D. Sontag. Using Anchors to Estimate Clinical State without Labeled Data. American Medical Informatics Association (AMIA) Annual Symposium, Nov. 2014. [Slides] BibTex
- N. Silberman, D. Sontag, R. Fergus. Instance Segmentation of Indoor Scenes using a Coverage Loss. European Conference on Computer Vision (ECCV), Sept. 2014. BibTex
- X. Wang, D. Sontag, F. Wang. Unsupervised Learning of Disease Progression Models. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2014. [Slides] BibTex
- H. Bui, T. Huynh, D. Sontag. Lifted Tree-Reweighted Variational Inference. Uncertainty in Artificial Intelligence (UAI), July 2014. Addendum BibTex
- A. Weller, K. Tang, D. Sontag, T. Jebara. Understanding the Bethe approximation: when and how can it go wrong? Uncertainty in Artificial Intelligence (UAI), July 2014. BibTex
- Y. Jernite, Y. Halpern, D. Sontag. Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. Neural Information Processing Systems (NIPS) 26, Dec. 2013. Supplementary [Code] BibTex
- Y. Jernite, Y. Halpern, S. Horng, D. Sontag. Predicting Chief Complaints at Triage Time in the Emergency Department. NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare, Dec. 2013. BibTex
- E. Brenner, D. Sontag. SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. Uncertainty in Artificial Intelligence (UAI) 29, July 2013. BibTex [arXiv]
- Y. Halpern, D. Sontag. Unsupervised Learning of Noisy-Or Bayesian Networks. Uncertainty in Artificial Intelligence (UAI) 29, July 2013. BibTex
- S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, M. Zhu. A Practical Algorithm for Topic Modeling with Provable Guarantees. 30th International Conference on Machine Learning (ICML), 2013. Supplementary Video BibTex
- D. Sontag, D. K. Choe, Y. Li. Efficiently Searching for Frustrated Cycles in MAP Inference. Uncertainty in Artificial Intelligence (UAI) 28, Aug. 2012. [code] Supplementary BibTex
- Y. Halpern, S. Horng, L. A. Nathanson, N. I. Shapiro, D. Sontag. A Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text. ICML 2012 Workshop on Clinical Data Analysis, July 2012. BibTex
- D. Sontag, K. Collins-Thompson, P. N. Bennett, R. W. White, S. Dumais, B. Billerbeck. Probabilistic Models for Personalizing Web Search. Fifth ACM International Conference on Web Search and Data Mining (WSDM), Feb. 2012. [Slides] BibTex
- D. Sontag, D. Roy. Complexity of Inference in Latent Dirichlet Allocation. Neural Information Processing Systems (NIPS)
24, Dec. 2011. [Slides] BibTex
- K. Collins-Thompson, P. N. Bennett, R. W. White, S. de la Chica, D. Sontag. Personalizing Web Search Results by Reading Level. Twentieth ACM International Conference on Information and Knowledge Management (CIKM 2011), Oct. 2011. BibTex
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D. Sontag, A. Globerson, T. Jaakkola. Introduction to Dual Decomposition for Inference. Optimization for Machine Learning, editors S. Sra, S. Nowozin, and S. J. Wright: MIT Press, 2011. BibTex
- D. Sontag, O. Meshi, T. Jaakkola,
A. Globerson. More data means less
inference: A pseudo-max approach to structured learning. Neural Information Processing Systems (NIPS)
23, Dec. 2010. Supplementary BibTex
- T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag. Dual Decomposition for Parsing with Non-Projective Head Automata. Empirical Methods in Natural Language Processing (EMNLP), 2010. Best paper award. BibTex
- A. Rush, D. Sontag, M. Collins, and T. Jaakkola. On Dual Decomposition and
Linear Programming
Relaxations for
Natural Language
Processing. Empirical Methods in Natural Language Processing (EMNLP), 2010. BibTex
- O. Meshi, D. Sontag, T. Jaakkola, A. Globerson. Learning Efficiently with Approximate Inference via Dual Losses. 27th International Conference on Machine Learning (ICML), July 2010. BibTex
- T. Jaakkola, D. Sontag, A. Globerson,
M. Meila. Learning
Bayesian Network Structure using LP Relaxations. 13th International Conference on Artificial Intelligence
and Statistics (AI-STATS),
2010. BibTex
- D. Sontag, T. Jaakkola. Tree Block Coordinate Descent for MAP in Graphical Models. 12th International Conference on Artificial Intelligence and Statistics (AI-STATS), April 2009. BibTex
- D. Sontag, Y. Zhang, A. Phanishayee, D. Andersen, D. Karger. Scaling All-Pairs Overlay Routing. Fifth ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT), Dec. 2009. Code BibTex
- D. Sontag, A. Globerson, T. Jaakkola. Clusters and Coarse Partitions in LP Relaxations. Neural Information Processing Systems
(NIPS) 21, Dec. 2008. BibTex
- D. Sontag, T. Meltzer, A. Globerson, Y. Weiss, T. Jaakkola. Tightening
LP Relaxations for MAP using Message Passing. Uncertainty
in Artificial Intelligence (UAI) 24, July 2008. Best paper award. [code] BibTex
- D. Sontag, T. Jaakkola. New
Outer Bounds on the Marginal Polytope. Neural Information Processing Systems
(NIPS) 20, Dec. 2007. Outstanding student paper award. Addendum BibTex
- D. Sontag, R. Singh, B. Berger. Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments. Pacific Symposium on Biocomputing (PSB), 2007. Supplementary information BibTex
- B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov. BLOG:
Probabilistic Models with Unknown Objects. In Lise Getoor
and Ben Taskar, eds. Statistical Relational Learning. Cambridge, MA:
MIT Press, 2007.
- B. Milch, B. Marthi, S. Russell, D. Sontag,
D. L. Ong, and A. Kolobov. BLOG:
Probabilistic Models with Unknown Objects. Proc. 19th
International Joint Conference on Artificial Intelligence (IJCAI):
1352-1359, 2005. BibTex
- B. Milch, B. Marthi, D. Sontag, S. Russell,
D. L. Ong, and A. Kolobov. Approximate
Inference for Infinite Contingent Bayesian Networks. 10th
International Workshop on Artificial Intelligence and
Statistics, 2005. BibTex
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