Sontag, David

68 publications

CHIL 2025 How Does My Language Model Understand Clinical Text? Furong Jia, David Sontag, Monica Agrawal

CLeaR 2025 Probably Approximately Correct High-Dimensional Causal Effect Estimation Given a Valid Adjustment Set Davin Choo, Chandler Squires, Arnab Bhattacharyya, David Sontag

TMLR 2025 The RealHumanEval: Evaluating Large Language Models’ Abilities to Support Programmers Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David Sontag

CHIL 2024 A Data-Centric Approach to Generate Faithful and High Quality Patient Summaries with Large Language Models Stefan Hegselmann, Zejiang Shen, Florian Gierse, Monica Agrawal, David Sontag, Xiaoyi Jiang

AISTATS 2024 Benchmarking Observational Studies with Experimental Data Under Right-Censoring Ilker Demirel, Edward De Brouwer, Zeshan M Hussain, Michael Oberst, Anthony A Philippakis, David Sontag

NeurIPS 2024 Med-Real2Sim: Non-Invasive Medical Digital Twins Using Physics-Informed Self-Supervised Learning Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, Ahmed Alaa

ICML 2024 Prediction-Powered Generalization of Causal Inferences Ilker Demirel, Ahmed Alaa, Anthony Philippakis, David Sontag

NeurIPS 2024 Theoretical Analysis of Weak-to-Strong Generalization Hunter Lang, David Sontag, Aravindan Vijayaraghavan

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

AISTATS 2023 Conformalized Unconditional Quantile Regression Ahmed M. Alaa, Zeshan Hussain, David Sontag

NeurIPS 2023 Effective Human-AI Teams via Learned Natural Language Rules and Onboarding Hussein Mozannar, Jimin Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag

AISTATS 2023 Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag

CHIL 2023 Large-Scale Study of Temporal Shift in Health Insurance Claims Christina X Ji, Ahmed M Alaa, David Sontag

NeurIPSW 2023 Simulating Iterative Human-AI Interaction in Programming with LLMs Hussein Mozannar, Valerie Chen, Dennis Wei, Prasanna Sattigeri, Manish Nagireddy, Subhro Das, Ameet Talwalkar, David Sontag

AISTATS 2023 TabLLM: Few-Shot Classification of Tabular Data with Large Language Models Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David Sontag

AISTATS 2023 Who Should Predict? Exact Algorithms for Learning to Defer to Humans Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag

AISTATS 2022 Leveraging Time Irreversibility with Order-Contrastive Pre-Training Monica N. Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David Sontag

AISTATS 2022 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 Sontag, Fredrik Johansson

ICML 2022 Co-Training Improves Prompt-Based Learning for Large Language Models Hunter Lang, Monica N Agrawal, Yoon Kim, David Sontag

NeurIPS 2022 ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography Ahmed M. Alaa, Anthony Philippakis, David Sontag

NeurIPS 2022 Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Nikolaj Thams, Michael Oberst, David Sontag

ICMLW 2022 Evaluating Robustness to Dataset Shift via Parametric Robustness Sets Michael Oberst, Nikolaj Thams, David Sontag

NeurIPS 2022 Falsification Before Extrapolation in Causal Effect Estimation Zeshan M Hussain, Michael Oberst, Ming-Chieh Shih, David Sontag

JMLR 2022 Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag

ICML 2022 Sample Efficient Learning of Predictors That Complement Humans Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, Samira Samadi

NeurIPS 2022 Training Subset Selection for Weak Supervision Hunter Lang, Aravindan Vijayaraghavan, David Sontag

AISTATS 2021 Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances Hunter Lang, Aravind Reddy, David Sontag, Aravindan Vijayaraghavan

AISTATS 2021 PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming Alexander Lew, Monica Agrawal, David Sontag, Vikash Mansinghka

MLHC 2021 Directing Human Attention in Event Localization for Clinical Timeline Creation Jason Zhao, Monica Agrawal, Pedram Razavi, David Sontag

NeurIPS 2021 Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leora Horwitz, David Sontag

ICML 2021 Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch) Hunter Lang, David Sontag, Aravindan Vijayaraghavan

ICML 2021 Neural Pharmacodynamic State Space Modeling Zeshan M Hussain, Rahul G. Krishnan, David Sontag

ICML 2021 Regularizing Towards Causal Invariance: Linear Models with Proxies Michael Oberst, Nikolaj Thams, Jonas Peters, David Sontag

AISTATS 2020 Characterization of Overlap in Observational Studies Michael Oberst, Fredrik Johansson, Dennis Wei, Tian Gao, Gabriel Brat, David Sontag, Kush Varshney

ICML 2020 Consistent Estimators for Learning to Defer to an Expert Hussein Mozannar, David Sontag

ICML 2020 Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag

ICML 2020 Estimation of Bounds on Potential Outcomes for Decision Making Maggie Makar, Fredrik Johansson, John Guttag, David Sontag

MLHC 2020 Fast, Structured Clinical Documentation via Contextual Autocomplete Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger, David Sontag

MLHC 2020 Knowledge Base Completion for Constructing Problem-Oriented Medical Records James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David Sontag

MLHC 2020 Robust Benchmarking for Machine Learning of Clinical Entity Extraction Monica Agrawal, Chloe O’Connell, Yasmin Fatemi, Ariel Levy, David Sontag

AISTATS 2019 Block Stability for MAP Inference Hunter Lang, David Sontag, Aravindan Vijayaraghavan

ICML 2019 Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models Michael Oberst, David Sontag

MLHC 2019 Few-Shot Learning for Dermatological Disease Diagnosis Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chaplain, David Sontag, Xavier Amatriain

AISTATS 2019 Overcomplete Independent Component Analysis via SDP Anastasia Podosinnikova, Amelia Perry, Alexander S. Wein, Francis Bach, Alexandre d’Aspremont, David Sontag

AISTATS 2019 Support and Invertibility in Domain-Invariant Representations Fredrik D. Johansson, David Sontag, Rajesh Ranganath

JMLR 2019 Train and Test Tightness of LP Relaxations in Structured Prediction Ofer Meshi, Ben London, Adrian Weller, David Sontag

ICML 2018 Semi-Amortized Variational Autoencoders Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, Alexander Rush

NeurIPS 2018 Why Is My Classifier Discriminatory? Irene Chen, Fredrik D Johansson, David Sontag

NeurIPS 2017 Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling

ICML 2017 Estimating Individual Treatment Effect: Generalization Bounds and Algorithms Uri Shalit, Fredrik D. Johansson, David Sontag

ICML 2017 Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation Yacine Jernite, Anna Choromanska, David Sontag

MLHC 2016 Clinical Tagging with Joint Probabilistic Models Yoni Halpern, Steven Horng, David Sontag

MLHC 2016 Identifiable Phenotyping Using Constrained Non-Negative Matrix Factorization Shalmali Joshi, Suriya Gunasekar, David Sontag, Ghosh Joydeep

ICML 2016 Learning Representations for Counterfactual Inference Fredrik Johansson, Uri Shalit, David Sontag

MLHC 2016 Multi-Task Prediction of Disease Onsets from Longitudinal Laboratory Tests Narges Razavian, Jake Marcus, David Sontag

ICML 2016 Train and Test Tightness of LP Relaxations in Structured Prediction Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag

ICML 2015 A Fast Variational Approach for Learning Markov Random Field Language Models Yacine Jernite, Alexander Rush, David Sontag

NeurIPS 2015 Barrier Frank-Wolfe for Marginal Inference Rahul G Krishnan, Simon Lacoste-Julien, David Sontag

ICML 2015 How Hard Is Inference for Structured Prediction? Amir Globerson, Tim Roughgarden, David Sontag, Cafer Yildirim

ICML 2013 A Practical Algorithm for Topic Modeling with Provable Guarantees Sanjeev Arora, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu

NeurIPS 2013 Discovering Hidden Variables in Noisy-or Networks Using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag

NeurIPS 2011 Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy

AISTATS 2010 Learning Bayesian Network Structure Using LP Relaxations Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila

NeurIPS 2010 More Data Means Less Inference: A Pseudo-Max Approach to Structured Learning David Sontag, Ofer Meshi, Amir Globerson, Tommi S. Jaakkola

AISTATS 2009 Tree Block Coordinate Descent for MAP in Graphical Models David Sontag, Tommi Jaakkola

NeurIPS 2008 Clusters and Coarse Partitions in LP Relaxations David Sontag, Amir Globerson, Tommi S. Jaakkola

NeurIPS 2007 New Outer Bounds on the Marginal Polytope David Sontag, Tommi S. Jaakkola

AISTATS 2005 Approximate Inference for Infinite Contingent Bayesian Networks Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov