| 2017 | AMIA | Computational Phenotyping on Diverse Data Sources. | Jimeng Sun, Bradley A. Malin, Abel N. Kho, Mark W. Craven, Joydeep Ghosh |
| 2016 | AMIA | Modeling the Temporal Evolution of Postoperative Complications. | Shara I. Feld, Alexander G. Cobian, Sarah E. Tevis, Gregory D. Kennedy, Mark W. Craven |
| 2012 | AMIA | Learning to Predict Post-Hospitalization VTE Risk from EHR Data. | Emily Kawaler, Alexander G. Cobian, Peggy L. Peissig, Deanna S. Cross, Steven Yale, Mark W. Craven |
| 2000 | ICML | Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. | Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner |
| 2000 | ISMB | A Probabilistic Learning Approach to Whole-Genome Operon Prediction. | Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner |
| 1994 | ICML | Using Sampling and Queries to Extract Rules from Trained Neural Networks. | Mark W. Craven, Jude W. Shavlik |
| 1993 | ICML | Learning Symbolic Rules Using Artificial Neural Networks. | Mark W. Craven, Jude W. Shavlik |
| 1993 | IJCAI | Learning to Represent Codons: A Challenge Problem for Constructive Induction. | Mark W. Craven, Jude W. Shavlik |
| 1991 | ICML | Constructive Induction in Knowledge-Based Neural Networks. | Geoffrey G. Towell, Mark W. Craven, Jude W. Shavlik |