Generalization with Taxonomic Information. - Alan M. Frisch; C. David Page Jr. (AAAI, 1990)

Learning Constrained Atoms. - C. David Page Jr.; Alan M. Frisch (ML, 1991)

Generalizing Atoms in Constraint Logic. - C. David Page Jr.; Alan M. Frisch (KR, 1991)

Learnability in Inductive Logic Programrning: Some Basic Results and Techniques. - Michael Frazier; C. David Page Jr. (AAAI, 1993)

Object-oriented development of expert systems. - Daniela Mehandjiska; David Page (ANNES, 1993)

Development of an Intelligent Object-Oriented CASE Tool. - Daniela Mehandjiska-Stavreva; David Page; Paul K. Clark (OOIS, 1994)

An Abstract Definition of Graphical Notations for Object-Oriented Information Systems. - David Page; Paul K. Clark; Daniela Mehandjiska-Stavreva (OOIS, 1994)

Prefix Grammars: An Alternative Characterization of the Regular Languages. - Michael Frazier; C. David Page Jr. (Inf. Process. Lett., 1994)

Building Theories into Instantiation. - Alan M. Frisch; C. David Page Jr. (IJCAI, 1995)

A Learnability Model for Universal Representations and Its Application to Top-down Induction of Decision Trees. - Stephen H. Muggleton; David Page (Machine Intelligence 15, 1995)

Template Generator for a Methodology Independent Object-Oriented Case Tool. - Daniela Mehandjiska-Stavreva; David Page; Jonanthan Ham (OOIS, 1995)

Polynomial Learnability and Inductive Logic Programming: Methods and Results. - William W. Cohen; C. David Page Jr. (New Gener. Comput., 1995)

An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming. - Stephen H. Muggleton; David Page; Ashwin Srinivasan 001 (Inductive Logic Programming Workshop, 1996)

Meta-Modelling and Methodology Support in Object-Oriented CASE Tools. - Daniela Mehandjiska-Stavreva; David Page; Mi Duk Choi (OOIS, 1996)

Guest Editors' Introduction. - Stephen H. Muggleton; David Page (Mach. Learn., 1997)

The Design of the Client User Interface for a Meta Object-Oriented CASE Tool. - Chris Phillips; Steven Adams; David Page; Daniela Mehandjiska (TOOLS (28), 1998)

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL. - Paul W. Finn; Stephen H. Muggleton; David Page; Ashwin Srinivasan 001 (Mach. Learn., 1998)

Guest Editors' Introduction: Inductive Logic Programming. - Stephen H. Muggleton; David Page (J. Log. Program., 1999)

ILP: Just Do It. - David Page (Computational Logic, 2000)

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 (ICML, 2000)

A Probabilistic Learning Approach to Whole-Genome Operon Prediction. - Mark W. Craven; David Page; Jude W. Shavlik; Joseph Bockhorst; Jeremy D. Glasner (ISMB, 2000)

Parallel data mining for pharmacophore discovery. - James Graham; C. David Page; Alan Wild (SMC, 2000)

An Approach to Parallel Data Mining for Pharmacophore Discovery. - Ahmed H. Kamal; James H. Graham; C. David Page Jr. (IS, 2001)

Multiple Instance Regression. - Soumya Ray; David Page (ICML, 2001)

Guest Editor's Introduction. - David Page; James H. Graham (Int. J. Comput. Their Appl., 2001)

An Empirical Evaluation of Bagging in Inductive Logic Programming. - Inês de Castro Dutra; David Page; Vítor Santos Costa; Jude W. Shavlik (ILP, 2002)

Lattice-Search Runtime Distributions May Be Heavy-Tailed. - Filip Zelezný; Ashwin Srinivasan 001; David Page (ILP, 2002)

Modelling regulatory pathways in E. coli from time series expression profiles. - Irene M. Ong; Jeremy D. Glasner; David Page (ISMB, 2002)

KDD Cup 2001 Report. - Jie Cheng; Christos Hatzis; Hisashi Hayashi; Mark-A. Krogel; Shinichi Morishita; David Page; Jun Sese (SIGKDD Explor., 2002)

Accelerating the Drug Design Process through Parallel Inductive Logic Programming Data Mining. - James H. Graham; C. David Page Jr.; Ahmed H. Kamal (CSB, 2003)

Toward Automatic Management of Embarrassingly Parallel Applications. - Inês de Castro Dutra; David Page; Vítor Santos Costa; Jude W. Shavlik; Michael Waddell (Euro-Par, 2003)

Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction. - David Page; Soumya Ray (IJCAI, 2003)

The Role of Declarative Languages in Mining Biological Databases. - C. David Page Jr. (PADL, 2003)

CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. - Vítor Santos Costa; David Page; Maleeha Qazi; James Cussens (UAI, 2003)

A Bayesian Network Approach to Operon Prediction. - Joseph Bockhorst; Mark W. Craven; David Page; Jude W. Shavlik; Jeremy D. Glasner (Bioinform., 2003)

A Parallel Inductive Logic Programming Data Mining System for Drug Discovery. - Ahmed H. Kamal; James H. Graham; C. David Page Jr. (Int. J. Comput. Their Appl., 2003)

ILP: A Short Look Back and a Longer Look Forward. - David Page; Ashwin Srinivasan 001 (J. Mach. Learn. Res., 2003)

Biological applications of multi-relational data mining. - David Page; Mark Craven (SIGKDD Explor., 2003)

Sequential skewing: an improved skewing algorithm. - Soumya Ray; David Page (ICML, 2004)

A Monte Carlo Study of Randomised Restarted Search in ILP. - Filip Zelezný; Ashwin Srinivasan 001; David Page (ILP, 2004)

Using Machine Learning to Design and Interpret Gene-Expression Microarrays. - Michael Molla; Michael Waddell; David Page; Jude W. Shavlik (AI Mag., 2004)

Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. - Elizabeth S. Burnside; Jesse Davis; Vítor Santos Costa; Inês de Castro Dutra; Charles E. Kahn Jr.; Jason Fine; David Page (AMIA, 2005)

An Integrated Approach to Learning Bayesian Networks of Rules. - Jesse Davis; Elizabeth S. Burnside; Inês de Castro Dutra; David Page; Vítor Santos Costa (ECML, 2005)

Mode Directed Path Finding. - Irene M. Ong; Inês de Castro Dutra; David Page; Vítor Santos Costa (ECML, 2005)

Multi-instance tree learning. - Hendrik Blockeel; David Page; Ashwin Srinivasan 001 (ICML, 2005)

Generalized skewing for functions with continuous and nominal attributes. - Soumya Ray; David Page (ICML, 2005)

Why skewing works: learning difficult Boolean functions with greedy tree learners. - Bernard Rosell; Lisa Hellerstein; Soumya Ray; David Page (ICML, 2005)

View Learning for Statistical Relational Learning: With an Application to Mammography. - Jesse Davis; Elizabeth S. Burnside; Inês de Castro Dutra; David Page; Raghu Ramakrishnan 001; Vítor Santos Costa; Jude W. Shavlik (IJCAI, 2005)

A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment. - Héctor Corrada Bravo; David Page; Raghu Ramakrishnan 001; Jude W. Shavlik; Vítor Santos Costa (ILP, 2005)

Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma. - Michael Waddell; David Page; John D. Shaughnessy Jr. (BIOKDD, 2005)

An Efficient Approximation to Lookahead in Relational Learners. - Jan Struyf; Jesse Davis; C. David Page Jr. (ECML, 2006)

Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming. - Irene M. Ong; Scott E. Topper; C. David Page Jr.; Vítor Santos Costa (ILP, 2006)

ILP Through Propositionalization and Stochastic k-Term DNF Learning. - Aline Paes; Filip Zelezný; Gerson Zaverucha; C. David Page Jr.; Ashwin Srinivasan 001 (ILP, 2006)

Experimental Design of Time Series Data for Learning from Dynamic Bayesian Networks. - C. David Page Jr.; Irene M. Ong (Pacific Symposium on Biocomputing, 2006)

Quantitative pharmacophore models with inductive logic programming. - Ashwin Srinivasan 001; David Page; Rui Camacho; Ross D. King (Mach. Learn., 2006)

Randomised restarted search in ILP. - Filip Zelezný; Ashwin Srinivasan 001; C. David Page Jr. (Mach. Learn., 2006)

An integrated approach to feature invention and model construction for drug activity prediction. - Jesse Davis; Vítor Santos Costa; Soumya Ray; David Page (ICML, 2007)

Change of Representation for Statistical Relational Learning. - Jesse Davis; Irene M. Ong; Jan Struyf; Elizabeth S. Burnside; David Page; Vítor Santos Costa (IJCAI, 2007)

Using dynamic programming to create isotopic distribution maps from mass spectra. - Sean McIlwain; David Page; Edward L. Huttlin; Michael R. Sussman (ISMB/ECCB (Supplement of Bioinformatics), 2007)

Learning Bayesian Network Structure from Correlation-Immune Data. - Eric Lantz; Soumya Ray; David Page (UAI, 2007)

CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. - Vítor Santos Costa; David Page; James Cussens (Probabilistic Inductive Logic Programming, 2008)

Matching isotopic distributions from metabolically labeled samples. - Sean McIlwain; David Page; Edward L. Huttlin; Michael R. Sussman (ISMB, 2008)

A logic-based diagram of signalling pathways central to macrophage activation. - Sobia Raza; Kevin A. Robertson; Paul A. Lacaze; David Page; Anton J. Enright; Peter Ghazal; Tom C. Freeman (BMC Syst. Biol., 2008)

Information Extraction for Clinical Data Mining: A Mammography Case Study. - Houssam Nassif; Ryan W. Woods; Elizabeth S. Burnside; Mehmet Ayvaci; Jude W. Shavlik; David Page (ICDM Workshops, 2009)

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. - Houssam Nassif; Hassan Al-Ali; Sawsan Khuri; Walid Keirouz; David Page (ILP, 2009)

Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions. - Lisa Hellerstein; Bernard Rosell; Eric Bach 001; Soumya Ray; David Page (J. Mach. Learn. Res., 2009)

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. - Houssam Nassif; David Page; Mehmet Ayvaci; Jude W. Shavlik; Elizabeth S. Burnside (IHI, 2010)

Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. - Trevor Walker; Ciaran O'Reilly; Gautam Kunapuli; Sriraam Natarajan; Richard Maclin; David Page; Jude W. Shavlik (ILP, 2010)

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. - Ryan W. Woods; Louis Oliphant; Kazuhiko Shinki; David Page; Jude W. Shavlik; Elizabeth S. Burnside (J. Digit. Imaging, 2010)

Biomedical Informatics. - C. David Page Jr.; Sriraam Natarajan (Encyclopedia of Machine Learning, 2010)

Integrating knowledge capture and supervised learning through a human-computer interface. - Trevor Walker; Gautam Kunapuli; Noah Larsen; David Page; Jude W. Shavlik (K-CAP, 2011)

Identifying Adverse Drug Events by Relational Learning. - David Page; Vítor Santos Costa; Sriraam Natarajan; Aubrey Barnard; Peggy L. Peissig; Michael Caldwell (AAAI, 2012)

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. - Houssam Nassif; Yirong Wu; David Page; Elizabeth S. Burnside (AMIA, 2012)

A collective ranking method for genome-wide association studies. - Jie Liu 006; Elizabeth S. Burnside; Humberto J. Vidaillet; David Page (BCB, 2012)

Extracting BI-RADS features from Portuguese clinical texts. - Houssam Nassif; Filipe Cunha; Inês C. Moreira; Ricardo Cruz-Correia; Eliana Sousa; David Page; Elizabeth S. Burnside; Inês de Castro Dutra (BIBM, 2012)

Predicting atrial fibrillation and flutter using Electronic Health Records. - Shreyas Karnik; Sin Lam Tan; Bess Berg; Ingrid Glurich; Jinfeng Zhang; Humberto J. Vidaillet; C. David Page; Rajesh Chowdhary (EMBC, 2012)

Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. - Jeremy C. Weiss; Sriraam Natarajan; Peggy L. Peissig; Catherine A. McCarty; David Page (IAAI, 2012)

Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. - Kendrick Boyd; Jesse Davis; David Page; Vítor Santos Costa (ICML, 2012)

Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. - Jesse Davis; Vítor Santos Costa; Elizabeth Berg; David Page; Peggy L. Peissig; Michael Caldwell (ICML, 2012)

Multiplicative Forests for Continuous-Time Processes. - Jeremy C. Weiss; Sriraam Natarajan; David Page (NIPS, 2012)

Relational Differential Prediction. - Houssam Nassif; Vítor Santos Costa; Elizabeth S. Burnside; David Page (ECML/PKDD (1), 2012)

Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. - Jie Liu 006; Chunming Zhang; Catherine A. McCarty; Peggy L. Peissig; Elizabeth S. Burnside; David Page (UAI, 2012)

Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. - Jeremy C. Weiss; Sriraam Natarajan; Peggy L. Peissig; Catherine A. McCarty; David Page (AI Mag., 2012)

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. - José Carlos Almeida Santos; Houssam Nassif; David Page; Stephen H. Muggleton; Michael J. E. Sternberg (BMC Bioinform., 2012)

High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. - Jie Liu 006; Chunming Zhang; Catherine A. McCarty; Peggy L. Peissig; Elizabeth S. Burnside; David Page (AISTATS, 2012)

Learning When to Reject an Importance Sample. - Jeremy C. Weiss; Sriraam Natarajan; C. David Page Jr. (AAAI (Late-Breaking Developments), 2013)

Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. - Jie Liu 006; David Page; Houssam Nassif; Jude W. Shavlik; Peggy L. Peissig; Catherine A. McCarty; Adedayo A. Onitilo; Elizabeth S. Burnside (AMIA, 2013)

On Differentially Private Inductive Logic Programming. - Chen Zeng; Eric Lantz; Jeffrey F. Naughton; David Page (ILP, 2013)

Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models. - Jie Liu 006; David Page (NIPS, 2013)

Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals. - Kendrick Boyd; Kevin H. Eng; C. David Page Jr. (ECML/PKDD (3), 2013)

Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals. - Kendrick Boyd; Kevin H. Eng; C. David Page Jr. (ECML/PKDD (3), 2013)

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. - Houssam Nassif; Finn Kuusisto; Elizabeth S. Burnside; David Page; Jude W. Shavlik; Vítor Santos Costa (ECML/PKDD (3), 2013)

Forest-Based Point Process for Event Prediction from Electronic Health Records. - Jeremy C. Weiss; David Page (ECML/PKDD (3), 2013)

Learning Heterogeneous Hidden Markov Random Fields. - Jie Liu 006; Chunming Zhang; Elizabeth S. Burnside; David Page (AISTATS, 2014)

Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. - Yirong Wu; Jie Liu 006; David Page; Peggy L. Peissig; Catherine A. McCarty; Adedayo A. Onitilo; Elizabeth S. Burnside (AMIA, 2014)

Multiple Testing under Dependence via Semiparametric Graphical Models. - Jie Liu 006; Chunming Zhang; Elizabeth S. Burnside; David Page (ICML, 2014)

Support Vector Machines for Differential Prediction. - Finn Kuusisto; Vítor Santos Costa; Houssam Nassif; Elizabeth S. Burnside; David Page; Jude W. Shavlik (ECML/PKDD (2), 2014)

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. - Matthew Fredrikson; Eric Lantz; Somesh Jha; Simon M. Lin; David Page; Thomas Ristenpart (USENIX Security Symposium, 2014)

Relational machine learning for electronic health record-driven phenotyping. - Peggy L. Peissig; Vítor Santos Costa; Michael Caldwell; Carla Rottscheit; Richard L. Berg; Eneida A. Mendonça; David Page (J. Biomed. Informatics, 2014)

QuickFOIL: Scalable Inductive Logic Programming. - Qiang Zeng 002; Jignesh M. Patel; David Page (Proc. VLDB Endow., 2014)

Predicting Adverse Drug Events from Electronic Medical Records. - Jesse Davis; Vítor Santos Costa; Peggy L. Peissig; Michael Caldwell; David Page (Foundations of Biomedical Knowledge Representation, 2015)

Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks. - Jeremy C. Weiss; Sriraam Natarajan; C. David Page Jr. (AAAI, 2015)

Extracting Adverse Drug Events from Text Using Human Advice. - Phillip Odom; Vishal Bangera; Tushar Khot; David Page; Sriraam Natarajan (AIME, 2015)

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. - Jeremy C. Weiss; Finn Kuusisto; Kendrick Boyd; Jie Liu 006; David Page (AMIA, 2015)

Differential Privacy for Classifier Evaluation. - Kendrick Boyd; Eric Lantz; David Page (AISec@CCS, 2015)

Subsampled Exponential Mechanism: Differential Privacy in Large Output Spaces. - Eric Lantz; Kendrick Boyd; David Page (AISec@CCS, 2015)

Big Data in Healthcare: Opportunities and Challenges. - Mark W. Craven; C. David Page (Big Data, 2015)

Sparse modeling of spatial environmental variables associated with asthma. - Timothy S. Chang; Ronald E. Gangnon; C. David Page; William R. Buckingham; Aman Tandias; Kelly J. Cowan; Carrie D. Tomasallo; Brian G. Arndt; Lawrence P. Hanrahan; Theresa W. Guilbert (J. Biomed. Informatics, 2015)

Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. - Zhaobin Kuang; James A. Thomson; Michael Caldwell; Peggy L. Peissig; Ron M. Stewart; David Page (IJCAI, 2016)

Computational Drug Repositioning Using Continuous Self-Controlled Case Series. - Zhaobin Kuang; James A. Thomson; Michael Caldwell; Peggy L. Peissig; Ron M. Stewart; David Page (KDD, 2016)

Structure-Leveraged Methods in Breast Cancer Risk Prediction. - Jun Fan; Yirong Wu; Ming Yuan 001; David Page; Jie Liu 006; Irene M. Ong; Peggy L. Peissig; Elizabeth S. Burnside (J. Mach. Learn. Res., 2016)

Relational Learning for Sustainable Health. - Sriraam Natarajan; Peggy L. Peissig; David Page (Computational Sustainability, 2016)

Identifying Parkinson's Patients: A Functional Gradient Boosting Approach. - Devendra Singh Dhami; Ameet Soni; David Page; Sriraam Natarajan (AIME, 2017)

bigNN: An open-source big data toolkit focused on biomedical sentence classification. - Ahmad Pahlavan Tafti; Ehsun Behravesh; Mehdi Assefi; Eric LaRose; Jonathan C. Badger; John Mayer; AnHai Doan; David Page; Peggy L. Peissig (IEEE BigData, 2017)

SCCS for Detection of Differences in Brand and Generic Adverse Drug Events. - Jonathan C. Badger; Eric R. LaRose; Ross Kleiman; Richard L. Berg; James G. Linneman; Richard Hansen; David Page; Peggy L. Peissig (CRI, 2017)

A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications. - Finn Kuusisto; John W. Steill; Zhaobin Kuang; James A. Thomson; David Page; Ron M. Stewart (CRI, 2017)

Breast Cancer Risk Prediction Using Electronic Health Records. - Yirong Wu; Elizabeth S. Burnside; Jennifer Cox; Jun Fan; Ming Yuan 001; Jie Yin; Peggy L. Peissig; Alexander G. Cobian; David Page; Mark W. Craven (ICHI, 2017)

Employing spaceborne multispectral stereo pairs and pedestrian flow modeling to support disaster response activities in urban environments. - Dave Kelbe; Devin White; David Page; Kristin Safi; Andrew Hardin; Amy N. Rose (IGARSS, 2017)

Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data. - Zhaobin Kuang; Peggy L. Peissig; Vítor Santos Costa; Richard Maclin; David Page (KDD, 2017)

Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. - Yujia Bao; Zhaobin Kuang; Peggy L. Peissig; David Page; Rebecca Willett (MLHC, 2017)

A Screening Rule for l1-Regularized Ising Model Estimation. - Zhaobin Kuang; Sinong Geng; David Page (NIPS, 2017)

Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. - Stéphanie Boué; Thomas E. Exner; Samik Ghosh; Vincenzo Belcastro; Joh Dokler; David Page; Akash R. Boda; Filipe Bonjour; Barry J. Hardy; Patrick Vanscheeuwijck; Julia Hoeng; Manuel C. Peitsch (F1000Research, 2017)

Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption. - Irene Giacomelli; Somesh Jha; Marc Joye; C. David Page; Kyonghwan Yoon (IACR Cryptol. ePrint Arch., 2017)

Privacy-Preserving Ridge Regression on Distributed Data. - Irene Giacomelli; Somesh Jha; C. David Page; Kyonghwan Yoon (IACR Cryptol. ePrint Arch., 2017)

Markov logic networks for adverse drug event extraction from text. - Sriraam Natarajan; Vishal Bangera; Tushar Khot; Jose Picado; Anurag Wazalwar; Vítor Santos Costa; David Page; Michael Caldwell (Knowl. Inf. Syst., 2017)

Biomedical Informatics. - C. David Page; Sriraam Natarajan (Encyclopedia of Machine Learning and Data Mining, 2017)

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. - Shara Feld; Kaitlin M. Woo; Roxana Alexandridis; Yirong Wu; Jie Liu 006; Peggy L. Peissig; Adedayo A. Onitilo; Jennifer Cox; David Page; Elizabeth S. Burnside (AMIA, 2018)

Use of Electronic Health Record to Predict Family Relationships for Phenome-wide Research. - Xiayuan Huang; Robert C. Elston; John Mayer; Zhan Ye; Yuqi He; David Page; Scott J. Hebbring (AMIA, 2018)

Temporal Poisson Square Root Graphical Models. - Sinong Geng; Zhaobin Kuang; Peggy L. Peissig; David Page (ICML, 2018)

Recursive Feature Elimination by Sensitivity Testing. - Nicholas Sean Escanilla; Lisa Hellerstein; Ross Kleiman; Zhaobin Kuang; James Shull; David Page (ICMLA, 2018)

Quantifying predictive capability of electronic health records for the most harmful breast cancer. - Yirong Wu; Jun Fan; Peggy L. Peissig; Richard L. Berg; Ahmad Pahlavan Tafti; Jie Yin; Ming Yuan 001; David Page; Jennifer Cox; Elizabeth S. Burnside (Image Perception, Observer Performance, and Technology Assessment, 2018)

Causal Structure Learning via Temporal Markov Networks. - Aubrey Barnard; David Page (PGM, 2018)

Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error. - Sinong Geng; Zhaobin Kuang; Jie Liu 006; Stephen J. Wright 001; David Page (UAI, 2018)

Applying family analyses to electronic health records to facilitate genetic research. - Xiayuan Huang; Robert C. Elston; Guilherme J. M. Rosa; John Mayer; Zhan Ye; Terrie E. Kitchner; Murray H. Brilliant; David Page; Scott J. Hebbring (Bioinform., 2018)

Privacy-Preserving Collaborative Prediction using Random Forests. - Irene Giacomelli; Somesh Jha; Ross Kleiman; David Page; Kyonghwan Yoon (CoRR, 2018)

AUCμ: A Performance Metric for Multi-Class Machine Learning Models. - Ross Kleiman; David Page (ICML, 2019)

Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues. - Finn Kuusisto; Vítor Santos Costa; Zhonggang Hou; James A. Thomson; David Page; Ron M. Stewart (ICMLA, 2019)

High-Throughput Machine Learning from Electronic Health Records. - Ross S. Kleiman; Paul S. Bennett; Peggy L. Peissig; Richard L. Berg; Zhaobin Kuang; Scott J. Hebbring; Michael D. Caldwell; David Page (CoRR, 2019)

Predicting Drug-Drug Interactions from Molecular Structure Images. - Devendra Singh Dhami; Gautam Kunapuli; David Page; Sriraam Natarajan (CoRR, 2019)

Machine learning for phenotyping opioid overdose events. - Jonathan C. Badger; Eric LaRose; John Mayer; Fereshteh S. Bashiri; David Page; Peggy L. Peissig (J. Biomed. Informatics, 2019)

Adverse drug reaction discovery from electronic health records with deep neural networks. - Wei Zhang 058; Zhaobin Kuang; Peggy L. Peissig; David Page (CHIL, 2020)

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. - Wei Zhang 058; Thomas Kobber Panum; Somesh Jha; Prasad Chalasani; David Page (ICML, 2020)

AutoBlock: A Hands-off Blocking Framework for Entity Matching. - Wei Zhang 058; Hao Wei; Bunyamin Sisman; Xin Luna Dong; Christos Faloutsos; David Page (WSDM, 2020)

High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records. - Alexander K. Taylor 001; Ross Kleiman; Scott J. Hebbring; Peggy L. Peissig; David Page (CoRR, 2020)

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications. - Finn Kuusisto; Daniel Ng; John W. Steill; Ian Ross; Miron Livny; James A. Thomson; David Page; Ron M. Stewart (F1000Research, 2020)

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit. - Mark Coletti; Alex Fafard; David Page (IBM J. Res. Dev., 2020)

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. - Devendra Singh Dhami; Siwen Yan; Gautam Kunapuli; David Page; Sriraam Natarajan (AIME, 2021)

E-Pedigrees: a large-scale automatic family pedigree prediction application. - Xiayuan Huang; Nicholas P. Tatonetti; Katie Larow; Brooke Delgoffe; John Mayer; David Page; Scott J. Hebbring (Bioinform., 2021)

Advancing artificial intelligence-assisted pre-screening for fragile X syndrome. - Arezoo Movaghar; David Page; Murray H. Brilliant; Marsha Mailick (BMC Medical Informatics Decis. Mak., 2022)

Variable importance matching for causal inference. - Quinn Lanners; Harsh Parikh; Alexander Volfovsky; Cynthia Rudin; David Page (UAI, 2023)

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. - Quinn Lanners; Harsh Parikh; Alexander Volfovsky; Cynthia Rudin; David Page (CoRR, 2023)

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. - Boyao Li; Alexandar J. Thomson; Matthew M. Engelhard; David Page (CoRR, 2023)

Neural Markov Prolog. - Alexander Thomson; David Page (CoRR, 2023)

MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance. - Jihye Choi; Nils Palumbo; Prasad Chalasani; Matthew M. Engelhard; Somesh Jha; Anivarya Kumar; David Page (MLHC, 2024)

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. - Boyao Li; Alexander Thomson; Houssam Nassif; Matthew Engelhard; David Page (NeurIPS, 2024)

Differentially Private Multi-Site Treatment Effect Estimation. - Tatsuki Koga; Kamalika Chaudhuri; David Page (SaTML, 2024)

Soft phenotyping for sepsis via EHR time-aware soft clustering. - Shiyi Jiang; Xin Gai; Miriam M. Treggiari; William W. Stead; Yuankang Zhao; C. David Page; Anru R. Zhang (J. Biomed. Informatics, 2024)

Closing the Water Balance with a Precision Small-Scale Field Lysimeter. - Brad F. Lyles; Brad D. Sion; David Page; Jackson B. Crews; Eric V. McDonald; Mark B. Hausner (Sensors, 2024)

Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before-After Studies: Discovering ADEs in Generics. - Aubrey Barnard; Peggy L. Peissig; David Page (CLeaR, 2025)

Cosmos World Foundation Model Platform for Physical AI. - Niket Agarwal; Arslan Ali; Maciej Bala; Yogesh Balaji; Erik Barker; Tiffany Cai; Prithvijit Chattopadhyay; Yongxin Chen; Yin Cui; Yifan Ding; Daniel Dworakowski; Jiaojiao Fan; et al. (CoRR, 2025)

Training Video Foundation Models with NVIDIA NeMo. - Zeeshan Patel; Ethan He; Parth Mannan; Xiaowei Ren; Ryan Wolf; Niket Agarwal; Jacob Samuel Huffman; Zhuoyao Wang; Carl Wang; Jack Chang; Yan Bai; Tommy Huang; et al. (CoRR, 2025)

Probably Approximately Correct Causal Discovery. - Mian Wei; Somesh Jha; David Page (CoRR, 2025)