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C. David Page Jr. – C. David Page
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- affiliation: Duke University, Durham, NC, USA
- affiliation (former): University of Wisconsin-Madison, Madison, USA
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Journal Articles
- 2022
[j30]Arezoo Movaghar, David Page, Murray H. Brilliant, Marsha Mailick:
Advancing artificial intelligence-assisted pre-screening for fragile X syndrome. BMC Medical Informatics Decis. Mak. 22(1): 152 (2022)- 2021
- [j29]Xiayuan Huang
, Nicholas P. Tatonetti, Katie Larow, Brooke Delgoffee, John Mayer, David Page, Scott J. Hebbring:
E-Pedigrees: a large-scale automatic family pedigree prediction application. Bioinform. 37(21): 3966-3968 (2021) - 2020
- [j28]Mark Coletti, Alex Fafard, David Page:
Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit. IBM J. Res. Dev. 64(3/4): 1-12 (2020) - 2019
- [j27]Jonathan C. Badger, Eric LaRose, John Mayer, Fereshteh S. Bashiri, David Page, Peggy L. Peissig:
Machine learning for phenotyping opioid overdose events. J. Biomed. Informatics 94 (2019) - 2018
- [j26]Xiayuan Huang, Robert C. Elston, Guilherme J. M. Rosa, John Mayer, Zhan Ye, Terrie E. Kitchner, Murray H. Brilliant, David Page, Scott J. Hebbring:
Applying family analyses to electronic health records to facilitate genetic research. Bioinform. 34(4): 635-642 (2018) - 2017
- [j25]Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vítor Santos Costa, David Page, Michael Caldwell:
Markov logic networks for adverse drug event extraction from text. Knowl. Inf. Syst. 51(2): 435-457 (2017) - 2016
- [j24]Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M. Ong, Peggy L. Peissig, Elizabeth S. Burnside:
Structure-Leveraged Methods in Breast Cancer Risk Prediction. J. Mach. Learn. Res. 17: 85:1-85:15 (2016) - 2015
- [j23]Mark W. Craven, C. David Page:
Big Data in Healthcare: Opportunities and Challenges. Big Data 3(4): 209-210 (2015) - [j22]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:
Sparse modeling of spatial environmental variables associated with asthma. J. Biomed. Informatics 53: 320-329 (2015) - 2014
- [j21]Peggy L. Peissig, Vítor Santos Costa, Michael Caldwell, Carla Rottscheit, Richard L. Berg, Eneida A. Mendonça, David Page:
Relational machine learning for electronic health record-driven phenotyping. J. Biomed. Informatics 52: 260-270 (2014) - [j20]Qiang Zeng, Jignesh M. Patel, David Page:
QuickFOIL: Scalable Inductive Logic Programming. Proc. VLDB Endow. 8(3): 197-208 (2014) - 2012
- [j19]Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page:
Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records. AI Mag. 33(4): 33-45 (2012) - [j18]José Carlos Almeida Santos, Houssam Nassif, David Page, Stephen H. Muggleton, Michael J. E. Sternberg:
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. BMC Bioinform. 13: 162 (2012) - 2010
- [j17]Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside:
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. J. Digit. Imaging 23(5): 554-561 (2010) - 2009
- [j16]Lisa Hellerstein, Bernard Rosell, Eric Bach, Soumya Ray, David Page:
Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions. J. Mach. Learn. Res. 10: 2374-2411 (2009) - 2008
- [j15]Sobia Raza, Kevin A. Robertson, Paul A. Lacaze, David Page, Anton J. Enright, Peter Ghazal, Tom C. Freeman:
A logic-based diagram of signalling pathways central to macrophage activation. BMC Syst. Biol. 2: 36 (2008) - 2006
- [j14]Ashwin Srinivasan, David Page, Rui Camacho, Ross D. King:
Quantitative pharmacophore models with inductive logic programming. Mach. Learn. 64(1-3): 65-90 (2006) - [j13]Filip Zelezný, Ashwin Srinivasan, C. David Page Jr.:
Randomised restarted search in ILP. Mach. Learn. 64(1-3): 183-208 (2006) - 2004
- [j12]Michael Molla, Michael Waddell, David Page, Jude W. Shavlik:
Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Mag. 25(1): 23-44 (2004) - 2003
- [j11]Joseph Bockhorst, Mark W. Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner:
A Bayesian Network Approach to Operon Prediction. Bioinform. 19(10): 1227-1235 (2003) - [j10]Ahmed H. Kamal, James H. Graham, C. David Page Jr.:
A Parallel Inductive Logic Programming Data Mining System for Drug Discovery. Int. J. Comput. Their Appl. 10(3): 161-170 (2003) - [j9]David Page, Ashwin Srinivasan:
ILP: A Short Look Back and a Longer Look Forward. J. Mach. Learn. Res. 4: 415-430 (2003) - [j8]David Page, Mark Craven:
Biological applications of multi-relational data mining. SIGKDD Explor. 5(1): 69-79 (2003) - 2002
- [j7]Jie Cheng, Christos Hatzis, Hisashi Hayashi, Mark-A. Krogel, Shinichi Morishita, David Page, Jun Sese:
KDD Cup 2001 Report. SIGKDD Explor. 3(2): 47-64 (2002) - 2001
- [j6]David Page, James H. Graham:
Guest Editor's Introduction. Int. J. Comput. Their Appl. 8(2) (2001) - 1999
- [j5]Stephen H. Muggleton, David Page:
Guest Editors' Introduction: Inductive Logic Programming. J. Log. Program. 40(2-3): 125-126 (1999) - 1998
- [j4]Paul W. Finn, Stephen H. Muggleton, David Page, Ashwin Srinivasan:
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL. Mach. Learn. 30(2-3): 241-270 (1998) - 1997
- [j3]Stephen H. Muggleton, David Page:
Guest Editors' Introduction. Mach. Learn. 26(2-3): 97-98 (1997) - 1995
- [j2]William W. Cohen, C. David Page Jr.:
Polynomial Learnability and Inductive Logic Programming: Methods and Results. New Gener. Comput. 13(3&4): 369-409 (1995) - 1994
- [j1]Michael Frazier, C. David Page Jr.:
Prefix Grammars: An Alternative Characterization of the Regular Languages. Inf. Process. Lett. 51(2): 67-71 (1994)
Conference and Workshop Papers
- 2023
- [c109]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
Variable importance matching for causal inference. UAI 2023: 1174-1184 - 2021
- [c108]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. AIME 2021: 252-257 - 2020
- [c107]Wei Zhang, Zhaobin Kuang, Peggy L. Peissig, David Page:
Adverse drug reaction discovery from electronic health records with deep neural networks. CHIL 2020: 30-39 - [c106]Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page:
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. ICML 2020: 11235-11245 - [c105]Wei Zhang, Hao Wei, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, David Page:
AutoBlock: A Hands-off Blocking Framework for Entity Matching. WSDM 2020: 744-752 - 2019
- [c104]Ross Kleiman, David Page:
AUCμ: A Performance Metric for Multi-Class Machine Learning Models. ICML 2019: 3439-3447 - [c103]Finn Kuusisto, Vítor Santos Costa, Zhonggang Hou, James A. Thomson, David Page, Ron M. Stewart:
Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues. ICMLA 2019: 293-298 - 2018
- [c102]Irene Giacomelli, Somesh Jha, Marc Joye, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption. ACNS 2018: 243-261 - [c101]Shara Feld, Kaitlin M. Woo, Roxana Alexandridis, Yirong Wu, Jie Liu, Peggy L. Peissig, Adedayo A. Onitilo, Jennifer Cox, David Page, Elizabeth S. Burnside:
Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA 2018 - [c100]Xiayuan Huang, Robert C. Elston, John Mayer, Zhan Ye, Yuqi He, David Page, Scott J. Hebbring:
Use of Electronic Health Record to Predict Family Relationships for Phenome-wide Research. AMIA 2018 - [c99]Sinong Geng, Zhaobin Kuang, Peggy L. Peissig, David Page:
Temporal Poisson Square Root Graphical Models. ICML 2018: 1700-1709 - [c98]Nicholas Sean Escanilla, Lisa Hellerstein, Ross Kleiman, Zhaobin Kuang, James Shull, David Page:
Recursive Feature Elimination by Sensitivity Testing. ICMLA 2018: 40-47 - [c97]Yirong Wu, Jun Fan, Peggy L. Peissig, Richard L. Berg, Ahmad Pahlavan Tafti, Jie Yin, Ming Yuan, David Page, Jennifer Cox, Elizabeth S. Burnside:
Quantifying predictive capability of electronic health records for the most harmful breast cancer. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2018: 105770J - [c96]Aubrey Barnard, David Page:
Causal Structure Learning via Temporal Markov Networks. PGM 2018: 13-24 - [c95]Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen J. Wright, David Page:
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error. UAI 2018: 156-166 - 2017
- [c94]Devendra Singh Dhami, Ameet Soni, David Page, Sriraam Natarajan:
Identifying Parkinson's Patients: A Functional Gradient Boosting Approach. AIME 2017: 332-337 - [c93]Ahmad Pahlavan Tafti, Ehsun Behravesh, Mehdi Assefi, Eric LaRose, Jonathan C. Badger, John Mayer, AnHai Doan, David Page, Peggy L. Peissig:
bigNN: An open-source big data toolkit focused on biomedical sentence classification. IEEE BigData 2017: 3888-3896 - [c92]Jonathan C. Badger, Eric R. LaRose, Ross Kleiman, Richard L. Berg, James G. Linneman, Richard Hansen, David Page, Peggy L. Peissig:
SCCS for Detection of Differences in Brand and Generic Adverse Drug Events. CRI 2017 - [c91]Finn Kuusisto, John W. Steill, Zhaobin Kuang, James A. Thomson, David Page, Ron M. Stewart:
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications. CRI 2017 - [c90]Yirong Wu, Elizabeth S. Burnside, Jennifer Cox, Jun Fan, Ming Yuan, Jie Yin, Peggy L. Peissig, Alexander G. Cobian, David Page, Mark W. Craven:
Breast Cancer Risk Prediction Using Electronic Health Records. ICHI 2017: 224-228 - [c89]Dave Kelbe, Devin White, David Page, Kristin Safi, Andrew Hardin, Amy N. Rose:
Employing spaceborne multispectral stereo pairs and pedestrian flow modeling to support disaster response activities in urban environments. IGARSS 2017: 2573-2576 - [c88]Zhaobin Kuang, Peggy L. Peissig, Vítor Santos Costa, Richard Maclin, David Page:
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data. KDD 2017: 1537-1546 - [c87]Yujia Bao, Zhaobin Kuang, Peggy L. Peissig, David Page, Rebecca Willett:
Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data. MLHC 2017: 177-190 - [c86]Zhaobin Kuang, Sinong Geng, David Page:
A Screening Rule for l1-Regularized Ising Model Estimation. NIPS 2017: 720-731 - 2016
- [c85]Zhaobin Kuang, James A. Thomson, Michael Caldwell, Peggy L. Peissig, Ron M. Stewart, David Page:
Baseline Regularization for Computational Drug Repositioning with Longitudinal Observational Data. IJCAI 2016: 2521-2528 - [c84]Zhaobin Kuang, James A. Thomson, Michael Caldwell, Peggy L. Peissig, Ron M. Stewart, David Page:
Computational Drug Repositioning Using Continuous Self-Controlled Case Series. KDD 2016: 491-500 - 2015
- [c83]Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.:
Learning to Reject Sequential Importance Steps for Continuous-Time Bayesian Networks. AAAI 2015: 3628-3634 - [c82]Phillip Odom, Vishal Bangera, Tushar Khot, David Page, Sriraam Natarajan:
Extracting Adverse Drug Events from Text Using Human Advice. AIME 2015: 195-204 - [c81]Jeremy C. Weiss, Finn Kuusisto, Kendrick Boyd, Jie Liu, David Page:
Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. AMIA 2015 - [c80]Kendrick Boyd, Eric Lantz, David Page:
Differential Privacy for Classifier Evaluation. AISec@CCS 2015: 15-23 - [c79]Eric Lantz, Kendrick Boyd, David Page:
Subsampled Exponential Mechanism: Differential Privacy in Large Output Spaces. AISec@CCS 2015: 25-33 - 2014
- [c78]Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Learning Heterogeneous Hidden Markov Random Fields. AISTATS 2014: 576-584 - [c77]Yirong Wu, Jie Liu, David Page, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Comparing the Value of Mammographic Features and Genetic Variants in Breast Cancer Risk Prediction. AMIA 2014 - [c76]Jie Liu, Chunming Zhang, Elizabeth S. Burnside, David Page:
Multiple Testing under Dependence via Semiparametric Graphical Models. ICML 2014: 955-963 - [c75]Finn Kuusisto, Vítor Santos Costa, Houssam Nassif, Elizabeth S. Burnside, David Page, Jude W. Shavlik:
Support Vector Machines for Differential Prediction. ECML/PKDD (2) 2014: 50-65 - [c74]Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon M. Lin, David Page, Thomas Ristenpart:
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. USENIX Security Symposium 2014: 17-32 - 2013
- [c73]Jeremy C. Weiss, Sriraam Natarajan, C. David Page Jr.:
Learning When to Reject an Importance Sample. AAAI (Late-Breaking Developments) 2013 - [c72]Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA 2013 - [c71]Chen Zeng, Eric Lantz, Jeffrey F. Naughton, David Page:
On Differentially Private Inductive Logic Programming. ILP 2013: 18-30 - [c70]Jie Liu, David Page:
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models. NIPS 2013: 1232-1240 - [c69]Kendrick Boyd, Kevin H. Eng, C. David Page Jr.:
Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals. ECML/PKDD (3) 2013: 451-466 - [c68]Kendrick Boyd, Kevin H. Eng, C. David Page Jr.:
Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals. ECML/PKDD (3) 2013 - [c67]Jeremy C. Weiss, David Page:
Forest-Based Point Process for Event Prediction from Electronic Health Records. ECML/PKDD (3) 2013: 547-562 - [c66]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude W. Shavlik, Vítor Santos Costa:
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) 2013: 595-611 - 2012
- [c65]David Page, Vítor Santos Costa, Sriraam Natarajan, Aubrey Barnard, Peggy L. Peissig, Michael Caldwell:
Identifying Adverse Drug Events by Relational Learning. AAAI 2012: 1599-1605 - [c64]Houssam Nassif, Yirong Wu, David Page, Elizabeth S. Burnside:
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA 2012 - [c63]Jie Liu, Elizabeth S. Burnside, Humberto J. Vidaillet, David Page:
A collective ranking method for genome-wide association studies. BCB 2012: 313-320 - [c62]Houssam Nassif, Filipe Cunha, Inês C. Moreira, Ricardo Cruz-Correia, Eliana Sousa, David Page, Elizabeth S. Burnside, Inês de Castro Dutra:
Extracting BI-RADS features from Portuguese clinical texts. BIBM 2012: 1-4 - [c61]Shreyas Karnik, Sin Lam Tan, Bess Berg, Ingrid Glurich, Jinfeng Zhang, Humberto J. Vidaillet, C. David Page, Rajesh Chowdhary:
Predicting atrial fibrillation and flutter using Electronic Health Records. EMBC 2012: 5562-5565 - [c60]Jeremy C. Weiss, Sriraam Natarajan, Peggy L. Peissig, Catherine A. McCarty, David Page:
Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records. IAAI 2012: 2341-2347 - [c59]Kendrick Boyd, Jesse Davis, David Page, Vítor Santos Costa:
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. ICML 2012 - [c58]Jesse Davis, Vítor Santos Costa, Elizabeth Berg, David Page, Peggy L. Peissig, Michael Caldwell:
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. ICML 2012 - [c57]Jeremy C. Weiss, Sriraam Natarajan, David Page:
Multiplicative Forests for Continuous-Time Processes. NIPS 2012: 467-475 - [c56]Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page:
Relational Differential Prediction. ECML/PKDD (1) 2012: 617-632 - [c55]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. UAI 2012: 511-522 - [c54]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. AISTATS 2012: 712-721 - 2011
- [c53]Trevor Walker, Gautam Kunapuli, Noah Larsen, David Page, Jude W. Shavlik:
Integrating knowledge capture and supervised learning through a human-computer interface. K-CAP 2011: 89-96 - 2010
- [c52]Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside:
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI 2010: 76-82 - [c51]Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik:
Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. ILP 2010: 253-268 - 2009
- [c50]Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page:
Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops 2009: 37-42 - [c49]Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, David Page:
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. ILP 2009: 149-165 - 2008
- [c48]Sean McIlwain, David Page, Edward L. Huttlin, Michael R. Sussman:
Matching isotopic distributions from metabolically labeled samples. ISMB 2008: 339-347 - 2007
- [c47]Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page:
An integrated approach to feature invention and model construction for drug activity prediction. ICML 2007: 217-224 - [c46]Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa:
Change of Representation for Statistical Relational Learning. IJCAI 2007: 2719-2726 - [c45]Sean McIlwain, David Page, Edward L. Huttlin, Michael R. Sussman:
Using dynamic programming to create isotopic distribution maps from mass spectra. ISMB/ECCB (Supplement of Bioinformatics) 2007: 328-336 - [c44]Eric Lantz, Soumya Ray, David Page:
Learning Bayesian Network Structure from Correlation-Immune Data. UAI 2007: 235-242 - 2006
- [c43]Jan Struyf, Jesse Davis, C. David Page Jr.:
An Efficient Approximation to Lookahead in Relational Learners. ECML 2006: 775-782 - [c42]Irene M. Ong, Scott E. Topper, C. David Page Jr., Vítor Santos Costa:
Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming. ILP 2006: 366-378 - [c41]Aline Paes, Filip Zelezný, Gerson Zaverucha, C. David Page Jr., Ashwin Srinivasan:
ILP Through Propositionalization and Stochastic k-Term DNF Learning. ILP 2006: 379-393 - [c40]C. David Page Jr., Irene M. Ong:
Experimental Design of Time Series Data for Learning from Dynamic Bayesian Networks. Pacific Symposium on Biocomputing 2006: 267-278 - 2005
- [c39]Elizabeth S. Burnside, Jesse Davis, Vítor Santos Costa, Inês de Castro Dutra, Charles E. Kahn Jr., Jason Fine, David Page:
Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. AMIA 2005 - [c38]Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa:
An Integrated Approach to Learning Bayesian Networks of Rules. ECML 2005: 84-95 - [c37]Irene M. Ong, Inês de Castro Dutra, David Page, Vítor Santos Costa:
Mode Directed Path Finding. ECML 2005: 673-681 - [c36]Hendrik Blockeel, David Page, Ashwin Srinivasan:
Multi-instance tree learning. ICML 2005: 57-64 - [c35]Soumya Ray, David Page:
Generalized skewing for functions with continuous and nominal attributes. ICML 2005: 705-712 - [c34]Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page:
Why skewing works: learning difficult Boolean functions with greedy tree learners. ICML 2005: 728-735 - [c33]Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik:
View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI 2005: 677-683 - [c32]Héctor Corrada Bravo, David Page, Raghu Ramakrishnan, Jude W. Shavlik, Vítor Santos Costa:
A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment. ILP 2005: 69-86 - [c31]Michael Waddell, David Page, John D. Shaughnessy Jr.:
Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma. BIOKDD 2005: 21-28 - 2004
- [c30]Soumya Ray, David Page:
Sequential skewing: an improved skewing algorithm. ICML 2004 - [c29]Filip Zelezný, Ashwin Srinivasan, David Page:
A Monte Carlo Study of Randomised Restarted Search in ILP. ILP 2004: 341-358 - 2003
- [c28]James H. Graham, C. David Page Jr., Ahmed H. Kamal:
Accelerating the Drug Design Process through Parallel Inductive Logic Programming Data Mining. CSB 2003: 400-402 - [c27]Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik, Michael Waddell:
Toward Automatic Management of Embarrassingly Parallel Applications. Euro-Par 2003: 509-516 - [c26]David Page, Soumya Ray:
Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction. IJCAI 2003: 601-612 - [c25]C. David Page Jr.:
The Role of Declarative Languages in Mining Biological Databases. PADL 2003: 1 - [c24]Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. UAI 2003: 517-524 - 2002
- [c23]Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik:
An Empirical Evaluation of Bagging in Inductive Logic Programming. ILP 2002: 48-65 - [c22]Filip Zelezný, Ashwin Srinivasan, David Page:
Lattice-Search Runtime Distributions May Be Heavy-Tailed. ILP 2002: 333-345 - [c21]Irene M. Ong, Jeremy D. Glasner, David Page:
Modelling regulatory pathways in E. coli from time series expression profiles. ISMB 2002: 241-248 - 2001
- [c20]Ahmed H. Kamal, James H. Graham, C. David Page Jr.:
An Approach to Parallel Data Mining for Pharmacophore Discovery. IS 2001: 100-103 - [c19]Soumya Ray, David Page:
Multiple Instance Regression. ICML 2001: 425-432 - 2000
- [c18]David Page:
ILP: Just Do It. Computational Logic 2000: 25-40 - [c17]Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner:
Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. ICML 2000: 199-206 - [c16]David Page:
ILP: Just Do It. ILP 2000: 3-18 - [c15]Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner:
A Probabilistic Learning Approach to Whole-Genome Operon Prediction. ISMB 2000: 116-127 - [c14]James Graham, C. David Page, Alan Wild:
Parallel data mining for pharmacophore discovery. SMC 2000: 1894-1899 - 1998
- [c13]Chris Phillips, Steven Adams, David Page, Daniela Mehandjiska:
The Design of the Client User Interface for a Meta Object-Oriented CASE Tool. TOOLS (28) 1998: 156-167 - 1996
- [c12]Stephen H. Muggleton, David Page, Ashwin Srinivasan:
An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming. Inductive Logic Programming Workshop 1996: 25-40 - [c11]Daniela Mehandjiska-Stavreva, David Page, Mi Duk Choi:
Meta-Modelling and Methodology Support in Object-Oriented CASE Tools. OOIS 1996: 370-386 - 1995
- [c10]Alan M. Frisch, C. David Page Jr.:
Building Theories into Instantiation. IJCAI 1995: 1210-1216 - [c9]Stephen H. Muggleton, David Page:
A Learnability Model for Universal Representations and Its Application to Top-down Induction of Decision Trees. Machine Intelligence 15 1995: 248-267 - [c8]Daniela Mehandjiska-Stavreva, David Page, Jonanthan Ham:
Template Generator for a Methodology Independent Object-Oriented Case Tool. OOIS 1995: 232-247 - 1994
- [c7]Daniela Mehandjiska-Stavreva, David Page, Paul K. Clark:
Development of an Intelligent Object-Oriented CASE Tool. OOIS 1994: 215-226 - [c6]David Page, Paul K. Clark, Daniela Mehandjiska-Stavreva:
An Abstract Definition of Graphical Notations for Object-Oriented Information Systems. OOIS 1994: 266-276 - 1993
- [c5]Michael Frazier, C. David Page Jr.:
Learnability in Inductive Logic Programrning: Some Basic Results and Techniques. AAAI 1993: 93-98 - [c4]Daniela Mehandjiska, David Page:
Object-oriented development of expert systems. ANNES 1993: 168-172 - 1991
- [c3]C. David Page Jr., Alan M. Frisch:
Learning Constrained Atoms. ML 1991: 427-431 - [c2]C. David Page Jr., Alan M. Frisch:
Generalizing Atoms in Constraint Logic. KR 1991: 429-440 - 1990
- [c1]Alan M. Frisch, C. David Page Jr.:
Generalization with Taxonomic Information. AAAI 1990: 755-761
Parts in Books or Collections
- 2016
- [p3]Sriraam Natarajan, Peggy L. Peissig, David Page:
Relational Learning for Sustainable Health. Computational Sustainability 2016: 245-264 - 2015
- [p2]Jesse Davis, Vítor Santos Costa, Peggy L. Peissig, Michael Caldwell, David Page:
Predicting Adverse Drug Events from Electronic Medical Records. Foundations of Biomedical Knowledge Representation 2015: 243-257 - 2008
- [p1]Vítor Santos Costa, David Page, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. Probabilistic Inductive Logic Programming 2008: 156-188
Editorship
- 2000
- [e2]David Page, James H. Graham:
ISCA 9th International Conference on Intelligent Systems, ICIS 2000, June 15-17, 2000, the Galt House, Louisville, Kentucky, USA. ISCA 2000, ISBN 1-880843-33-1 [contents] - 1998
- [e1]David Page:
Inductive Logic Programming, 8th International Workshop, ILP-98, Madison, Wisconsin, USA, July 22-24, 1998, Proceedings. Lecture Notes in Computer Science 1446, Springer 1998, ISBN 3-540-64738-4 [contents]
Reference Works
- 2017
- [r2]C. David Page, Sriraam Natarajan:
Biomedical Informatics. Encyclopedia of Machine Learning and Data Mining 2017: 143-163 - 2010
- [r1]C. David Page Jr., Sriraam Natarajan:
Biomedical Informatics. Encyclopedia of Machine Learning 2010: 132
Informal and Other Publications
- 2023
- [i24]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR abs/2302.11715 (2023) - [i23]Boyao Li, Alexandar J. Thomson, Matthew M. Engelhard, David Page:
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. CoRR abs/2305.17583 (2023) - [i22]Tatsuki Koga, Kamalika Chaudhuri, David Page:
Differentially Private Multi-Site Treatment Effect Estimation. CoRR abs/2310.06237 (2023) - [i21]Alexander Thomson, David Page:
Neural Markov Prolog. CoRR abs/2312.01521 (2023) - 2021
- [i20]Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach. CoRR abs/2103.10916 (2021) - 2020
- [i19]Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page:
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods. CoRR abs/2002.07906 (2020) - [i18]Sinong Geng, Zhaobin Kuang, Jie Liu, Stephen J. Wright, David Page:
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error. CoRR abs/2005.06083 (2020) - [i17]Sinong Geng, Zhaobin Kuang, Peggy L. Peissig, David Page:
Temporal Poisson Square Root Graphical Models. CoRR abs/2005.06462 (2020) - [i16]Alexander K. Taylor, Ross Kleiman, Scott J. Hebbring, Peggy L. Peissig, David Page:
High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records. CoRR abs/2011.09497 (2020) - [i15]Finn Kuusisto, Daniel Ng, John W. Steill, Ian Ross, Miron Livny, James A. Thomson, David Page, Ron M. Stewart:
KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications. F1000Research 9: 832 (2020) - 2019
- [i14]Finn Kuusisto, Vítor Santos Costa, Zhonggang Hou, James A. Thomson, David Page, Ron M. Stewart:
Machine Learning to Predict Developmental Neurotoxicity with High-throughput Data from 2D Bio-engineered Tissues. CoRR abs/1905.02121 (2019) - [i13]Finn Kuusisto, John W. Steill, Zhaobin Kuang, James A. Thomson, David Page, Ron M. Stewart:
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications. CoRR abs/1906.05255 (2019) - [i12]Ross S. Kleiman, Paul S. Bennett, Peggy L. Peissig, Richard L. Berg, Zhaobin Kuang, Scott J. Hebbring, Michael D. Caldwell, David Page:
High-Throughput Machine Learning from Electronic Health Records. CoRR abs/1907.01901 (2019) - [i11]Devendra Singh Dhami, Gautam Kunapuli, David Page, Sriraam Natarajan:
Predicting Drug-Drug Interactions from Molecular Structure Images. CoRR abs/1911.06356 (2019) - [i10]Wei Zhang, Hao Wei, Bunyamin Sisman, Xin Luna Dong, Christos Faloutsos, David Page:
AutoBlock: A Hands-off Blocking Framework for Entity Matching. CoRR abs/1912.03417 (2019) - 2018
- [i9]Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, David Page:
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. CoRR abs/1810.04707 (2018) - [i8]Irene Giacomelli, Somesh Jha, Ross Kleiman, David Page, Kyonghwan Yoon:
Privacy-Preserving Collaborative Prediction using Random Forests. CoRR abs/1811.08695 (2018) - 2017
- [i7]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:
Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. F1000Research 6: 12- (2017) - [i6]Irene Giacomelli, Somesh Jha, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression on Distributed Data. IACR Cryptol. ePrint Arch. 2017: 707 (2017) - [i5]Irene Giacomelli, Somesh Jha, Marc Joye, C. David Page, Kyonghwan Yoon:
Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption. IACR Cryptol. ePrint Arch. 2017: 979 (2017) - 2012
- [i4]Kendrick Boyd, Vítor Santos Costa, Jesse Davis, David Page:
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. CoRR abs/1206.4667 (2012) - [i3]Eric Lantz, Soumya Ray, David Page:
Learning Bayesian Network Structure from Correlation-Immune Data. CoRR abs/1206.5271 (2012) - [i2]Jie Liu, Chunming Zhang, Catherine A. McCarty, Peggy L. Peissig, Elizabeth S. Burnside, David Page:
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies. CoRR abs/1210.4868 (2012) - [i1]Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. CoRR abs/1212.2519 (2012)
Coauthor Index
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