← Authors

David Sontag

71 papers · 2007–2025 · 10 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (12) πŸƒ Academic Marathon (18) 🌍 Conference Polyglot (10) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (29) 🌟 Keyword Trendsetter Combo (8) πŸ† Keyword Champion πŸ”¬ Deep Specialist (12) 🀝 Dynamic Duo (12) πŸ’Ž Century Club (71) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ”₯ Unstoppable (11) ❓ The Questioner (3) πŸ—ƒοΈ Keyword Collector (84) πŸ“ˆ Trend Setter

Conferences

ICML (18) AISTATS (16) NIPS (16) MLHC (9) AAAI (3) EMNLP (3) ACL (2) JMLR (2) CLEAR (1) IJCNLP (1)

Top co-authors

Hunter Lang (12) Michael Oberst (8) Monica Agrawal (7) Aravindan Vijayaraghavan (6) Hussein Mozannar (5) Yoon Kim (5) Uri Shalit (4) Fredrik Johansson (4) Amir Globerson (4) Tommi Jaakkola (3)

Keywords

causal inference (10) representation learning (8) map inference (8) domain adaptation (6) graphical model (6) structured prediction (5) lp relaxation (5) few-shot learning (4) observational study (4) variational inference (4) large language model (4) combinatorial optimization (4) linear programming relaxation (4) randomized controlled trial (3) latent variable model (3) electronic health record (3) linear programming (3) zero-shot learning (3) approximate inference (3) markov random field (3)

Papers

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set CLEAR 2025
Learning to Decode Collaboratively with Multiple Language Models ACL 2024
Benchmarking Observational Studies with Experimental Data under Right-Censoring AISTATS 2024
Prediction-powered Generalization of Causal Inferences ICML 2024
Theoretical Analysis of Weak-to-Strong Generalization NIPS 2024
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning NIPS 2024
Who Should Predict? Exact Algorithms For Learning to Defer to Humans AISTATS 2023
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes MLHC 2023
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding NIPS 2023
Conformalized Unconditional Quantile Regression AISTATS 2023
Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions AISTATS 2023
TabLLM: Few-shot Classification of Tabular Data with Large Language Models AISTATS 2023
ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography NIPS 2022
Using time-series privileged information for provably efficient learning of prediction models AISTATS 2022
Sample Efficient Learning of Predictors that Complement Humans ICML 2022
Co-training Improves Prompt-based Learning for Large Language Models ICML 2022
Leveraging Time Irreversibility with Order-Contrastive Pre-training AISTATS 2022
Teaching Humans When to Defer to a Classifier via Exemplars AAAI 2022
Clustering Interval-Censored Time-Series for Disease Phenotyping AAAI 2022
Large language models are few-shot clinical information extractors EMNLP 2022
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects JMLR 2022
Falsification before Extrapolation in Causal Effect Estimation NIPS 2022
Training Subset Selection for Weak Supervision NIPS 2022
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets NIPS 2022
Deep Contextual Clinical Prediction with Reverse Distillation AAAI 2021
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance NIPS 2021
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes ACL 2021
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming AISTATS 2021
Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances AISTATS 2021
Neural Pharmacodynamic State Space Modeling ICML 2021
Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch) ICML 2021
Regularizing towards Causal Invariance: Linear Models with Proxies ICML 2021
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes IJCNLP 2021
Directing Human Attention in Event Localization for Clinical Timeline Creation MLHC 2021
Estimation of Bounds on Potential Outcomes For Decision Making ICML 2020
Characterization of Overlap in Observational Studies AISTATS 2020
Robust Benchmarking for Machine Learning of Clinical Entity Extraction MLHC 2020
Fast, Structured Clinical Documentation via Contextual Autocomplete MLHC 2020
Knowledge Base Completion for Constructing Problem-Oriented Medical Records MLHC 2020
Consistent Estimators for Learning to Defer to an Expert ICML 2020
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models ICML 2020
Support and Invertibility in Domain-Invariant Representations AISTATS 2019
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models ICML 2019
Block Stability for MAP Inference AISTATS 2019
Overcomplete Independent Component Analysis via SDP AISTATS 2019
Few-Shot Learning for Dermatological Disease Diagnosis MLHC 2019
Train and Test Tightness of LP Relaxations in Structured Prediction JMLR 2019
Optimality of Approximate Inference Algorithms on Stable Instances AISTATS 2018
Why Is My Classifier Discriminatory? NIPS 2018
Semi-Amortized Variational Autoencoders ICML 2018
Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation ICML 2017
Estimating individual treatment effect: generalization bounds and algorithms ICML 2017
Causal Effect Inference with Deep Latent-Variable Models NIPS 2017
Tightness of LP Relaxations for Almost Balanced Models AISTATS 2016
Learning Representations for Counterfactual Inference ICML 2016
Train and Test Tightness of LP Relaxations in Structured Prediction ICML 2016
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization MLHC 2016
Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests MLHC 2016
Clinical Tagging with Joint Probabilistic Models MLHC 2016
How Hard is Inference for Structured Prediction? ICML 2015
Barrier Frank-Wolfe for Marginal Inference NIPS 2015
A Fast Variational Approach for Learning Markov Random Field Language Models ICML 2015
A Practical Algorithm for Topic Modeling with Provable Guarantees ICML 2013
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests NIPS 2013
Complexity of Inference in Latent Dirichlet Allocation NIPS 2011
Learning Bayesian Network Structure using LP Relaxations AISTATS 2010
Dual Decomposition for Parsing with Non-Projective Head Automata EMNLP 2010
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing EMNLP 2010
More data means less inference: A pseudo-max approach to structured learning NIPS 2010
Clusters and Coarse Partitions in LP Relaxations NIPS 2008
New Outer Bounds on the Marginal Polytope NIPS 2007