1997 · Machine Learning
Multitask Learning
6,350 citationsRead
Most cited
191 papers in total2006 · International Conference on Machine Learning
An empirical comparison of supervised learning algorithms
2,706 citationsRead
2006 · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Model compression
2,041 citationsRead
2005 · International Conference on Machine Learning
Predicting good probabilities with supervised learning
1,764 citationsRead
2015 · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Intelligible Models for HealthCare
1,665 citationsRead
2013 · arXiv (Cornell University)
Do Deep Nets Really Need to be Deep?
1,335 citationsRead
Newest first
191 papers in total2026
- SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden SignalsarXiv (Cornell University)
- Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation ModelsarXiv (Cornell University)
- Hypothesis Generation via Interpretable Machine Learning: A Case Study on Risk Factors for Postradiation Therapy Lung Cancer RecurrenceAdvances in Radiation Oncology
- A Machine Learning Approach to Determine the Optimal Age for Total Hip Arthroplasty: When Is Risk for Adverse Outcomes Lowest?Journal of the American Academy of Orthopaedic Surgeons
- The most important features in generalized additive models might be groups of featuresScientific Reports
- Exploring the optimal age for total knee arthroplasty to minimize risk of adverse outcomes: machine learning analysis of a statewide cohortArthroplasty
- Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsiesarXiv (Cornell University)
2025
- Comparing Community‐Level Social Determinants of Health With Patient Race in Total Hip Arthroplasty OutcomesArthritis Care & Research · 3 citations
- The hidden risk of round numbers and sharp thresholds in clinical practicenpj Digital Medicine · 1 citations
- Comparison of externally validated interpretable machine‐learning model with the United States Preventive Services Task Force approach to pre‐eclampsia risk assessmentUltrasound in Obstetrics and Gynecology · 1 citations
2024
- Rethinking Interpretability in the Era of Large Language ModelsarXiv (Cornell University) · 43 citations
- Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric populationAmerican Journal of Obstetrics & Gynecology MFM · 17 citations
- Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsarXiv (Cornell University) · 2 citations
- GAMformer: Bridging Tabular Foundation Models and Interpretable Machine LearningarXiv (Cornell University) · 1 citations
- Elephants Never Forget: Testing Language Models for Memorization of Tabular DataarXiv (Cornell University) · 1 citations
- 753 Interpretable Machine Learning Predictive Model to Understand RISK Factors for Early Onset Colorectal CancerGastroenterology · 1 citations
- Data Science with LLMs and Interpretable ModelsarXiv (Cornell University)
- Bridging Prediction and Causality: Deciphering Clinical Outcomes in Locally Advanced Non-Small Cell Lung Cancer Treated with Chemoradiation via Multitask Machine Learning and Causal Inference of Proton vs. Photon Radiotherapy ChoicesInternational Journal of Radiation Oncology*Biology*Physics
- Understanding AGE as a RISK Factor for Complications After Total KNEE Arthroplasty: WHAT CAN WE Learn from Machine Learning?Osteoarthritis and Cartilage
2023
- Augmenting interpretable models with large language models during trainingNature Communications · 61 citations
- Considerations when learning additive explanations for black-box modelsMachine Learning · 33 citations
- GAM Coach: Towards Interactive and User-centered Algorithmic RecourseCHI Conference on Human Factors in Computing Systems · 18 citations
- Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal OutcomesJournal of Healthcare Informatics Research · 14 citations
- Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?arXiv (Cornell University) · 4 citations
- Predicting severe maternal morbidity at admission for delivery using intelligible machine learningAmerican Journal of Obstetrics and Gynecology · 3 citations
- LLMs Understand Glass-Box Models, Discover Surprises, and Suggest RepairsarXiv (Cornell University) · 2 citations
- Preterm preeclampsia prediction using intelligible machine learningAmerican Journal of Obstetrics and Gynecology · 2 citations
- Unique insights into risk factors for antepartum stillbirth using explainable AIAmerican Journal of Obstetrics and Gynecology · 2 citations
- Understanding risk factors for shoulder dystocia using interpretable machine learningAmerican Journal of Obstetrics and Gynecology · 2 citations
- Extending Explainable Boosting Machines to Scientific Image DataarXiv (Cornell University) · 1 citations
Research in numbers
From OpenAlex- article60
- conference paper58
- preprint53
- book chapter7
- report3
- arXiv (Cornell University)48
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)7
- Lecture notes in computer science7
- American Journal of Obstetrics and Gynecology7
- International Conference on Machine Learning5
- Harsha Nori24
- Benjamin J. Lengerich19
- Yin Lou14
- Sarah Tan14
- Alexandru Niculescu-Mizil11
Experience and education
Universities
2026University of Maryland, Baltimore
2026Hofstra University
2023Duke University
2020University of Wisconsin–Madison
2001 to 2012Cornell University
2000University of Pittsburgh
2000University of California, Los Angeles
Industry and labs
2026Hospital for Special Surgery
2007 to 2025Microsoft (United States)
2020Google (United States)