Sontag, David

Recurrent Neural Networks for Multivariate Time Series with Missing Values

Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports

Time Series Analysis and ForecastingMachine Learning in Healthcare

Cited by 2,370

Character-Aware Neural Language Models

Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 1,530

Learning a Health Knowledge Graph from Electronic Medical Records

Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., & Sontag, D. (2017) · Scientific Reports

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 423

Learning Representations for Counterfactual Inference

Johansson, F. D., Shalit, U., & Sontag, D. (2016) · arXiv

Advanced Causal Inference TechniquesMachine Learning in Healthcare

Cited by 396

Guidelines for reinforcement learning in healthcare

Gottesman, O., Johansson, F., Komorowski, M., Faisal, A., Sontag, D., Doshi-Velez, F., & Celi, L. A. (2019) · Nature Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 395

BLOG: Probabilistic Models with Unknown Objects

Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D. L., & Kolobov, A. (2007) · Introduction to Statistical Relational Learning

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 390

Large language models are few-shot clinical information extractors

Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., & Sontag, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Machine Learning in HealthcareTopic Modeling

Cited by 358

Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning

Horng, S., Sontag, D. A., Halpern, Y., Jernite, Y., Shapiro, N. I., & Nathanson, L. A. (2017) · PLOS ONE

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 310

Deep Kalman Filters

Krishnan, R. G., Shalit, U., & Sontag, D. (2015) · arXiv

Gaussian Processes and Bayesian InferenceTarget Tracking and Data Fusion in Sensor Networks

Cited by 206

Why is my classifier discriminatory?

Chen, I., Johansson, F. D., & Sontag, D. (2018) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 185

Objective assessment of depressive symptoms with machine learning and wearable sensors data

Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., & Picard, R. (2017) · 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

Emotion and Mood RecognitionContext-Aware Activity Recognition Systems

Cited by 116

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

Jernite, Y., Bowman, S. R., & Sontag, D. (2017) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 101

Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes

Choi, M. Y., Chen, I., Clarke, A. E., Fritzler, M. J., Buhler, K. A., Urowitz, M., Hanly, J., St-Pierre, Y., Gordon, C., Bae, S.-C., et al. (2023) · Annals of the Rheumatic Diseases

Systemic Lupus Erythematosus ResearchSystemic Sclerosis and Related Diseases

Cited by 89

Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative

Levy, A., Agrawal, M., Satyanarayan, A., & Sontag, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)

Cited by 73

Support and Invertibility in Domain-Invariant Representations

Johansson, F. D., Sontag, D., & Ranganath, R. (2019) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 70

Consistent Estimators for Learning to Defer to an Expert

Mozannar, H., & Sontag, D. (2020) · arXiv

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 61

A Deep Dive into Single-Cell RNA Sequencing Foundation Models

Boiarsky, R., Singh, N., Buendia, A., Getz, G., & Sontag, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory)

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 43

Deeper evaluation of a single-cell foundation model

Boiarsky, R., Singh, N. M., Buendia, A., Amini, A. P., Getz, G., & Sontag, D. (2024) · Nature Machine Intelligence

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 25

Sample Efficient Learning of Predictors that Complement Humans

Charusaie, M.-A., Mozannar, H., Sontag, D., & Samadi, S. (2022) · arXiv

Machine Learning and AlgorithmsAdvanced Bandit Algorithms Research

Cited by 6

A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models

Hegselmann, S., Shen, S. Z., Gierse, F., Agrawal, M., Sontag, D., & Jiang, X. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 6

Teaching Humans When to Defer to a Classifier via Exemplars

Mozannar, H., Satyanarayan, A., & Sontag, D. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 5

Who Should Predict? Exact Algorithms For Learning to Defer to Humans

Mozannar, H., Lang, H., Wei, D., Sattigeri, P., Das, S., & Sontag, D. (2023) · arXiv

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 4