David A. Sontag
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
72
Venues
17
Active years
2005–2026
Best venue rank
A*
Where they publish
Papers
72 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | ACL | Scaling Collaborative Effort with Agents. | 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 | CHI | Need Help? Designing Proactive AI Assistants for Programming. | Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David A. Sontag, Ameet Talwalkar |
| 2024 | ACL | Learning to Decode Collaboratively with Multiple Language Models. | Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David A. Sontag |
| 2024 | AISTATS | Benchmarking Observational Studies with Experimental Data under Right-Censoring. | Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag |
| 2024 | ICML | Prediction-powered Generalization of Causal Inferences. | Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag |
| 2023 | AISTATS | Conformalized Unconditional Quantile Regression. | Ahmed M. Alaa, Zeshan M. Hussain, David A. Sontag |
| 2023 | AISTATS | TabLLM: Few-shot Classification of Tabular Data with Large Language Models. | Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag |
| 2023 | AISTATS | Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions. | Zeshan M. Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David A. Sontag |
| 2023 | AISTATS | Who Should Predict? Exact Algorithms For Learning to Defer to Humans. | Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag |
| 2022 | AAAI | Clustering Interval-Censored Time-Series for Disease Phenotyping. | Irene Y. Chen, Rahul G. Krishnan, David A. Sontag |
| 2022 | AAAI | Teaching Humans When to Defer to a Classifier via Exemplars. | Hussein Mozannar, Arvind Satyanarayan, David A. Sontag |
| 2022 | AISTATS | Leveraging Time Irreversibility with Order-Contrastive Pre-training. | Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag |
| 2022 | AISTATS | Using time-series privileged information for provably efficient learning of prediction models. | Rickard K. A. Karlsson, Martin Willbo, Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag, Fredrik Johansson |
| 2022 | EMNLP | Large language models are few-shot clinical information extractors. | Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag |
| 2022 | ICML | Sample Efficient Learning of Predictors that Complement Humans. | Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, Samira Samadi |
| 2022 | ICML | Co-training Improves Prompt-based Learning for Large Language Models. | Hunter Lang, Monica N. Agrawal, Yoon Kim, David A. Sontag |
| 2021 | AAAI | Deep Contextual Clinical Prediction with Reverse Distillation. | Rohan S. Kodialam, Rebecca Boiarsky, Justin Lim, Aditya Sai, Neil Dixit, David A. Sontag |
| 2021 | ACL | CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes. | James Mullenbach, Yada Pruksachatkun, Sean Adler, Jennifer Seale, Jordan Swartz, T. Greg McKelvey, Hui Dai, Yi Yang, David A. Sontag |
| 2021 | AISTATS | Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. | Hunter Lang, Aravind Reddy, David A. Sontag, Aravindan Vijayaraghavan |
| 2021 | AISTATS | PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. | Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka |
| 2021 | CHI | Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. | Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag |
| 2021 | ICML | Neural Pharmacodynamic State Space Modeling. | Zeshan M. Hussain, Rahul G. Krishnan, David A. Sontag |
| 2021 | ICML | Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2021 | ICML | Regularizing towards Causal Invariance: Linear Models with Proxies. | Michael Oberst, Nikolaj Thams, Jonas Peters, David A. Sontag |
| 2021 | UIST | MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. | Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger |
| 2020 | AISTATS | Characterization of Overlap in Observational Studies. | Michael Oberst, Fredrik D. Johansson, Dennis Wei, Tian Gao, Gabriel A. Brat, David A. Sontag, Kush R. Varshney |
| 2020 | ICML | Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models. | Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David A. Sontag |
| 2020 | ICML | Estimation of Bounds on Potential Outcomes For Decision Making. | Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag |
| 2020 | ICML | Consistent Estimators for Learning to Defer to an Expert. | Hussein Mozannar, David A. Sontag |
| 2020 | KDD | Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes. | Soorajnath Boominathan, Michael Oberst, Helen Zhou, Sanjat Kanjilal, David A. Sontag |
| 2020 | PSB | Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph. | Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag |
| 2019 | AISTATS | Support and Invertibility in Domain-Invariant Representations. | Fredrik D. Johansson, David A. Sontag, Rajesh Ranganath |
| 2019 | AISTATS | Block Stability for MAP Inference. | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2019 | AISTATS | Overcomplete Independent Component Analysis via SDP. | Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis R. Bach, Alexandre d'Aspremont, David A. Sontag |
| 2019 | ICML | Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. | Michael Oberst, David A. Sontag |
| 2018 | AISTATS | Optimality of Approximate Inference Algorithms on Stable Instances. | Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2018 | ICML | Semi-Amortized Variational Autoencoders. | Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush |
| 2018 | PSB | Cell-specific prediction and application of drug-induced gene expression . | 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 | UAI | Max-margin learning with the Bayes factor. | Rahul G. Krishnan, Arjun Khandelwal, Rajesh Ranganath, David A. Sontag |
| 2017 | AAAI | Structured Inference Networks for Nonlinear State Space Models. | Rahul G. Krishnan, Uri Shalit, David A. Sontag |
| 2017 | ACII | Objective assessment of depressive symptoms with machine learning and wearable sensors data. | Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David A. Sontag, Rosalind W. Picard |
| 2017 | ICML | Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation. | Yacine Jernite, Anna Choromanska, David A. Sontag |
| 2017 | ICML | Estimating individual treatment effect: generalization bounds and algorithms. | Uri Shalit, Fredrik D. Johansson, David A. Sontag |
| 2016 | AAAI | Character-Aware Neural Language Models. | Yoon Kim, Yacine Jernite, David A. Sontag, Alexander M. Rush |
| 2016 | AISTATS | Tightness of LP Relaxations for Almost Balanced Models. | Adrian Weller, Mark Rowland, David A. Sontag |
| 2016 | AMIA | Data Mining for Medical Informatics (DMMI) - Learning Health. | Fei Wang, Gregor Stiglic, Mihaela van der Schaar, David A. Sontag, Christopher C. Yang |
| 2016 | AMIA | Comparison of Approaches for Heart Failure Case Identification from EHR Data. | Saul Blecker, Stuart Katz, Leora Horwitz, Gilad J. Kuperman, Hannah Park, David A. Sontag |
| 2016 | ICML | Learning Representations for Counterfactual Inference. | Fredrik D. Johansson, Uri Shalit, David A. Sontag |
| 2016 | ICML | Train and Test Tightness of LP Relaxations in Structured Prediction. | Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David A. Sontag |
| 2015 | AMIA | Visual Exploration of Temporal Data in Electronic Medical Records. | Josua Krause, Narges Razavian, Enrico Bertini, David A. Sontag |
| 2015 | AMIA | Gaussian Processes for interpreting Multiple Prostate Specific Antigen measurements for Prostate Cancer Prediction. | Narges Razavian, Saul Blecker, David A. Sontag |
| 2015 | ICML | How Hard is Inference for Structured Prediction? | Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim |
| 2015 | ICML | A Fast Variational Approach for Learning Markov Random Field Language Models. | Yacine Jernite, Alexander M. Rush, David A. Sontag |
| 2014 | AMIA | Using Anchors to Estimate Clinical State without Labeled Data. | Yoni Halpern, Youngduck Choi, Steven Horng, David A. Sontag |
| 2014 | ECCV | Instance Segmentation of Indoor Scenes Using a Coverage Loss. | Nathan Silberman, David A. Sontag, Rob Fergus |
| 2014 | KDD | Unsupervised learning of disease progression models. | Xiang Wang, David A. Sontag, Fei Wang |
| 2014 | UAI | Lifted Tree-Reweighted Variational Inference. | Hung Hai Bui, Tuyen N. Huynh, David A. Sontag |
| 2014 | UAI | Understanding the Bethe Approximation: When and How can it go Wrong? | Adrian Weller, Kui Tang, Tony Jebara, David A. Sontag |
| 2013 | ICML | A Practical Algorithm for Topic Modeling with Provable Guarantees. | Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu |
| 2013 | UAI | SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure. | Eliot Brenner, David A. Sontag |
| 2013 | UAI | Unsupervised Learning of Noisy-Or Bayesian Networks. | Yonatan Halpern, David A. Sontag |
| 2012 | UAI | Efficiently Searching for Frustrated Cycles in MAP Inference. | David A. Sontag, Do Kook Choe, Yitao Li |
| 2012 | WSDM | Probabilistic models for personalizing web search. | David A. Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan T. Dumais, Bodo Billerbeck |
| 2011 | CIKM | Personalizing web search results by reading level. | Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David A. Sontag |
| 2010 | EMNLP | Dual Decomposition for Parsing with Non-Projective Head Automata. | Terry Koo, Alexander M. Rush, Michael Collins, Tommi S. Jaakkola, David A. Sontag |
| 2010 | EMNLP | On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. | Alexander M. Rush, David A. Sontag, Michael Collins, Tommi S. Jaakkola |
| 2010 | ICML | Learning Efficiently with Approximate Inference via Dual Losses. | Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson |
| 2009 | CoNEXT | Scaling all-pairs overlay routing. | David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger |
| 2008 | UAI | Tightening LP Relaxations for MAP using Message Passing. | David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss |
| 2007 | PSB | Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments. | David A. Sontag, Rohit Singh, Bonnie Berger |
| 2005 | AISTATS | Approximate Inference for Infinite Contingent Bayesian Networks. | Brian Milch, Bhaskara Marthi, David A. Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov |
| 2005 | IJCAI | BLOG: Probabilistic Models with Unknown Objects. | Brian Milch, Bhaskara Marthi, Stuart Russell, David A. Sontag, Daniel L. Ong, Andrey Kolobov |