Jude W. Shavlik

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik: Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference. Int. J. Semantic Web Inf. Syst. 8(3): 42-73 (2012)

Ameet Soni, Jude W. Shavlik: Probabilistic Ensembles for Improved Inference in protein-Structure Determination. J. Bioinformatics and Computational Biology 10(1) (2012)

Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik: Gradient-based boosting for statistical relational learning: The relational dependency network case. Machine Learning 86(1): 25-56 (2012)

Ce Zhang, Feng Niu, Christopher Ré, Jude W. Shavlik: Big Data versus the Crowd: Looking for Relationships in All the Right Places. ACL (1) 2012: 825-834

Feng Niu, Ce Zhang, Christopher Re, Jude W. Shavlik: Scaling Inference for Markov Logic via Dual Decomposition. ICDM 2012: 1032-1037

Gautam Kunapuli, Jude W. Shavlik: Mirror Descent for Metric Learning: A Unified Approach. ECML/PKDD (1) 2012: 859-874

Feng Niu, Ce Zhang, Christopher Re, Jude W. Shavlik: DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. VLDS 2012: 25-28

Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik: Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. PVLDB 4(6): 373-384 (2011)

Faisal Farooq, Balaji Krishnapuram, Rómer Rosales, Shipeng Yu, Jude W. Shavlik, Raju Kucherlapati: Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report. SIGHIT Record 1(1): 23-25 (2011)

Ameet Soni, Jude W. Shavlik: Probabilistic ensembles for improved inference in protein-structure determination. BCB 2011: 264-273

Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik: Learning Markov Logic Networks via Functional Gradient Boosting. ICDM 2011: 320-329

Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik: Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. IJCAI 2011: 1414-1420

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

Gautam Kunapuli, Richard Maclin, Jude W. Shavlik: Advice Refinement in Knowledge-Based SVMs. NIPS 2011: 1728-1736

Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik: Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. CoRR abs/1104.3216 (2011)

Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik: Felix: Scaling Inference for Markov Logic with an Operator-based Approach. CoRR abs/1108.0294 (2011)

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. Digital Imaging 23(5): 554-561 (2010)

Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik: Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. Statistical Relational Artificial Intelligence 2010

Ameet Soni, Craig A. Bingman, Jude W. Shavlik: Guiding belief propagation using domain knowledge for protein-structure determination. BCB 2010: 285-294

Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik: Multi-Agent Inverse Reinforcement Learning. ICMLA 2010: 395-400

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

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

Gautam Kunapuli, Kristin P. Bennett, Amina Shabbeer, Richard Maclin, Jude W. Shavlik: Online Knowledge-Based Support Vector Machines. ECML/PKDD (2) 2010: 145-161

Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik: Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) 2010: 434-450

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Transfer Learning via Advice Taking. Advances in Machine Learning I 2010: 147-170

Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik: Spherical-harmonic decomposition for molecular recognition in electron-density maps. IJDMB 3(2): 205-227 (2009)

Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma: Bellwether analysis: Searching for cost-effective query-defined predictors in large databases. TKDD 3(1) (2009)

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

Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik: Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. ICMLA 2009: 141-146

Jude W. Shavlik, Sriraam Natarajan: Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. IJCAI 2009: 1951-1956

Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik: Boosting First-Order Clauses for Large, Skewed Data Sets. ILP 2009: 166-177

Lisa Torrey, Jude W. Shavlik: Policy Transfer via Markov Logic Networks. ILP 2009: 234-248

Hendrik Blockeel, Jude W. Shavlik, Prasad Tadepalli: Guest editors' introduction: special issue on inductive logic programming (ILP-2007). Machine Learning 73(1): 1-2 (2008)

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Rule Extraction for Transfer Learning. Rule Extraction from Support Vector Machines 2008: 67-82

Hendrik Blockeel, Jan Ramon, Jude W. Shavlik, Prasad Tadepalli (Eds.): Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers. Lecture Notes in Computer Science 4894, Springer 2008, isbn 978-3-540-78468-5

Frank DiMaio, Dmitry A. Kondrashov, Eduard Bitto, Ameet Soni, Craig A. Bingman, George N. Phillips Jr., Jude W. Shavlik: Creating protein models from electron-density maps using particle-filtering methods. Bioinformatics 23(21): 2851-2858 (2007)

Richard Maclin, Edward W. Wild, Jude W. Shavlik, Lisa Torrey, Trevor Walker: Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming. AAAI 2007: 584-589

Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik: Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics. BIBM 2007: 258-265

Mark Goadrich, Jude W. Shavlik: Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. ILP 2007: 122-131

Louis Oliphant, Jude W. Shavlik: Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. ILP 2007: 191-199

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Relational Macros for Transfer in Reinforcement Learning. ILP 2007: 254-268

Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richard Maclin: Building Relational World Models for Reinforcement Learning. ILP 2007: 280-291

Mark Goadrich, Louis Oliphant, Jude W. Shavlik: Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves. Machine Learning 64(1-3): 231-261 (2006)

Richard Maclin, Jude W. Shavlik, Trevor Walker, Lisa Torrey: A Simple and Effective Method for Incorporating Advice into Kernel Methods. AAAI 2006: 427-432

Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin: Skill Acquisition Via Transfer Learning and Advice Taking. ECML 2006: 425-436

Frank DiMaio, Jude W. Shavlik: Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition. ICDM 2006: 845-850

Frank DiMaio, Jude W. Shavlik, George N. Phillips: A probabilistic approach to protein backbone tracing in electron density maps. ISMB (Supplement of Bioinformatics) 2006: 81-89

Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma: Bellwether Analysis: Predicting Global Aggregates from Local Regions. VLDB 2006: 655-666

Richard Maclin, Jude W. Shavlik, Lisa Torrey, Trevor Walker, Edward W. Wild: Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression. AAAI 2005: 819-824

Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin: Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. ECML 2005: 412-424

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

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

Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin: Knowledge transfer via advice taking. K-CAP 2005: 217-218

Michael Molla, Michael Waddell, David Page, Jude W. Shavlik: Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Magazine 25(1): 23-44 (2004)

Olvi L. Mangasarian, Jude W. Shavlik, Edward W. Wild: Knowledge-Based Kernel Approximation. Journal of Machine Learning Research 5: 1127-1141 (2004)

Michael Molla, Jude W. Shavlik, Thomas Albert, Todd Richmond, Steven Smith: A Self-Tuning Method for One-Chip SNP Identification. CSB 2004: 69-79

Jude W. Shavlik: Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text. ILP 2004: 7

Frank DiMaio, Jude W. Shavlik: Learning an Approximation to Inductive Logic Programming Clause Evaluation. ILP 2004: 80-97

Mark Goadrich, Louis Oliphant, Jude W. Shavlik: Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. ILP 2004: 98-115

Jude W. Shavlik, Mark Shavlik: Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. KDD 2004: 276-285

Frank DiMaio, Jude W. Shavlik, George N. Phillips: Pictorial Structures for Molecular Modeling: Interpreting Density Maps. NIPS 2004

Joseph Bockhorst, Mark Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner: A Bayesian Network Approach to Operon Prediction. Bioinformatics 19(10): 1227-1235 (2003)

Tina Eliassi-Rad, Jude W. Shavlik: A System for Building Intelligent Agents that Learn to Retrieve and Extract Information. User Model. User-Adapt. Interact. 13(1-2): 35-88 (2003)

Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik: Knowledge-Based Nonlinear Kernel Classifiers. COLT 2003: 102-113

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

Fernanda Araujo Baião, Marta Mattoso, Jude W. Shavlik, Gerson Zaverucha: Applying Theory Revision to the Design of Distributed Databases. ILP 2003: 57-74

Yolanda Gil, Mark A. Musen, Jude W. Shavlik: Report on the First International Conference on Knowledge Capture (K-CAP). AI Magazine 23(4): 107-108 (2002)

Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik: Interpreting microarray expression data using text annotating the genes. Inf. Sci. 146(1-4): 75-88 (2002)

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

J. B. Tobler, Michael Molla, Emile F. Nuwaysir, R. D. Green, Jude W. Shavlik: Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. ISMB 2002: 164-171

Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik: Interpreting Microarray Expression Data Using Text Annotating the Genes. JCIS 2002: 1224-1230

Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik: Knowledge-Based Support Vector Machine Classifiers. NIPS 2002: 521-528

Tina Eliassi-Rad, Jude W. Shavlik: A Theory-Refinement Approach to Information Extraction. ICML 2001: 130-137

Mark 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

Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner: A Probabilistic Learning Approach to Whole-Genome Operon Prediction. ISMB 2000: 116-127

Jeremy Goecks, Jude W. Shavlik: Learning users' interests by unobtrusively observing their normal behavior. IUI 2000: 129-132

Carolyn F. Allex, Jude W. Shavlik, Frederick R. Blattner: Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies. Bioinformatics 15(9): 723-728 (1999)

Jude W. Shavlik, Lawrence Birnbaum, William R. Swartout, Eric Horvitz, Barbara Hayes-Roth: Bridging Science and Applications (Panel). IUI 1999: 45-46

Jude W. Shavlik, Susan Calcari, Tina Eliassi-Rad, Jack Solock: An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web. IUI 1999: 157-160

Jude W. Shavlik (Ed.): Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998. Morgan Kaufmann 1998, isbn 1-55860-556-8

Mark Craven, Jude W. Shavlik: Understanding Time-Series Networks: A Case Study in Rule Extraction. Int. J. Neural Syst. 8(4): 373-384 (1997)

David W. Opitz, Jude W. Shavlik: Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. J. Artif. Intell. Res. (JAIR) 6: 177-209 (1997)

Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner: Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations. ISMB 1997: 3-14

David W. Opitz, Jude W. Shavlik: Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. CoRR cs.AI/9705102 (1997)

David W. Opitz, Jude W. Shavlik: Actively Searching for an Effective Neural Network Ensemble. Connect. Sci. 8(3): 337-354 (1996)

Richard Maclin, Jude W. Shavlik: Creating Advice-Taking Reinforcement Learners. Machine Learning 22(1-3): 251-281 (1996)

Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner: Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications. ISMB 1996: 3-14

Kevin J. Cherkauer, Jude W. Shavlik: Growing Simpler Decision Trees to Facilitate Knowledge Discovery. KDD 1996: 315-318

David W. Opitz, Jude W. Shavlik: Dynamically adding symbolically meaningful nodes to knowledge-based neural networks. Knowl.-Based Syst. 8(6): 301-311 (1995)

Jude W. Shavlik, Lawrence Hunter, David B. Searls: Introduction. Machine Learning 21(1-2): 5-9 (1995)

Richard Maclin, Jude W. Shavlik: Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks. IJCAI 1995: 524-531

Mark Craven, Jude W. Shavlik: Extracting Tree-Structured Representations of Trained Networks. NIPS 1995: 24-30

Kevin J. Cherkauer, Jude W. Shavlik: Rapid Quality Estimation of Neural Network Input Representations. NIPS 1995: 45-51

David W. Opitz, Jude W. Shavlik: Generating Accurate and Diverse Members of a Neural-Network Ensemble. NIPS 1995: 535-541

Geoffrey G. Towell, Jude W. Shavlik: Knowledge-Based Artificial Neural Networks. Artif. Intell. 70(1-2): 119-165 (1994)

David B. Searls, Jude W. Shavlik, Lawrence Hunter: The First International Conference on Intelligent Systems for Molecular Biology. AI Magazine 15(1): 12-13 (1994)

Mark Craven, Jude W. Shavlik: Machine Learning Approaches to Gene Recognition. IEEE Expert 9(2): 2-10 (1994)

Jude W. Shavlik: Combining Symbolic and Neural Learning. Machine Learning 14(1): 321-331 (1994)

Richard Maclin, Jude W. Shavlik: Incorporating Advice into Agents that Learn from Reinforcements. AAAI 1994: 694-699

Mark Craven, Jude W. Shavlik: Using Sampling and Queries to Extract Rules from Trained Neural Networks. ICML 1994: 37-45

David W. Opitz, Jude W. Shavlik: Using Genetic Search to Refine Knowledge-based Neural Networks. ICML 1994: 208-216

Richard Maclin, Jude W. Shavlik: Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Machine Learning 11: 195-215 (1993)

Geoffrey G. Towell, Jude W. Shavlik: Extracting Refined Rules from Knowledge-Based Neural Networks. Machine Learning 13: 71-101 (1993)

Mark Craven, Jude W. Shavlik: Learning Symbolic Rules Using Artificial Neural Networks. ICML 1993: 73-80

Mark Craven, Jude W. Shavlik: Learning to Represent Codons: A Challenge Problem for Constructive Induction. IJCAI 1993: 1319-1324

David W. Opitz, Jude W. Shavlik: Heuristically Expanding Knowledge-Based Neural Networks. IJCAI 1993: 1360-1365

Kevin J. Cherkauer, Jude W. Shavlik: Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools. ISMB 1993: 74-82

Lawrence Hunter, David B. Searls, Jude W. Shavlik (Eds.): Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology, Bethesda, MD, USA, July 1993. AAAI 1993, isbn 0-929280-47-4

Gary M. Scott, Jude W. Shavlik, W. Harmon Ray: Refining PID Controllers Using Neural Networks. Neural Computation 4(5): 746-757 (1992)

Richard Maclin, Jude W. Shavlik: Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. AAAI 1992: 165-170

Geoffrey G. Towell, Jude W. Shavlik: Using Symbolic Learning to Improve Knowledge-Based Neural Networks. AAAI 1992: 177-182

Jude W. Shavlik, Raymond J. Mooney, Geoffrey G. Towell: Symbolic and Neural Learning Algorithms: An Experimental Comparison. Machine Learning 6: 111-143 (1991)

Geoffrey G. Towell, Mark Craven, Jude W. Shavlik: Constructive Induction in Knowledge-Based Neural Networks. ML 1991: 213-217

Richard Maclin, Jude W. Shavlik: Refining Domain Theories Expressed as Finite-State Automata. ML 1991: 524-528

Gary M. Scott, Jude W. Shavlik, W. Harmon Ray: Refined PID Controllers Using Neural Networks. NIPS 1991: 555-562

Geoffrey G. Towell, Jude W. Shavlik: Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules. NIPS 1991: 977-984

Jude W. Shavlik, Gerald DeJong: Learning in Mathematically-Based Domains: Understanding and Generalizing Obstacle Cancellations. Artif. Intell. 45(1-2): 1-45 (1990)

Jude W. Shavlik: Acquiring Recursive and Iterative Concepts with Explanation-Based Learning. Machine Learning 5: 39-40 (1990)

Geoffrey G. Towell, Jude W. Shavlik, Michiel O. Noordewier: Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks. AAAI 1990: 861-866

Michiel O. Noordewier, Geoffrey G. Towell, Jude W. Shavlik: Training Knowledge-Based Neural Networks to Recognize Genes. NIPS 1990: 530-536

Jude W. Shavlik, Geoffrey G. Towell: Combining Explanation-Based Learning and Artificial Neural Networks. ML 1989: 90-93

Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell: Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML 1989: 169-173

Jude W. Shavlik: An Empirical Analysis of EBL Approaches for Learning Plan Schemata. ML 1989: 183-187

Richard Maclin, Jude W. Shavlik: Enriching Vocabularies by Generalizing Explanation Structures. ML 1989: 444-446

Jude W. Shavlik: Acquiring Recursive Concepts with Explanation-Based Learning. IJCAI 1989: 688-693

Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, Alan Gove: An Experimental Comparison of Symbolic and Connectionist Learning Algorithms. IJCAI 1989: 775-780

Jude W. Shavlik, Gerald DeJong: BAGGER: An EBL System that Extends and Generalizes Explanations. AAAI 1987: 516-520

Jude W. Shavlik, Gerald DeJong: An Explanation-based Approach to Generalizing Number. IJCAI 1987: 236-238

Jude W. Shavlik, Gerald DeJong: Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving. SYMSAC 1986: 148-153

Jude W. Shavlik: Learning about Momentum Conservation. IJCAI 1985: 667-669