Jude W. Shavlik

162 papers A* 33A 9B 18C 3Misc 2Journal 41Unranked 50

Year	Rank	Type	Title / Venue / Authors
2021	—	`conf`	A Recap of Early Work on Theory and Knowledge Refinement. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering Raymond J. Mooney, Jude W. Shavlik
2016	B	`conf`	Learning Relational Dependency Networks for Relation Extraction. ILP Ameet Soni, Dileep Viswanathan, Jude W. Shavlik, Sriraam Natarajan
2016	J	`jnl`	Learning Relational Dependency Networks for Relation Extraction. CoRR Dileep Viswanathan, Ameet Soni, Jude W. Shavlik, Sriraam Natarajan
2015	Misc	`conf`	Anomaly Detection in Text: The Value of Domain Knowledge. FLAIRS Raksha Kumaraswamy, Anurag Wazalwar, Tushar Khot, Jude W. Shavlik, Sriraam Natarajan
2015	J	`jnl`	Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. Mach. Learn. Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik
2015	—	`conf`	TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice. TAC Dileep Viswanathan, Anurag Wazalwar, Sriraam Natarajan, Ameet Soni, Jude W. Shavlik
2014	—	`book`	Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude W. Shavlik
2014	—	`conf`	Classification from One Class of Examples for Relational Domains. StarAI@AAAI Tushar Khot, Sriraam Natarajan, Jude W. Shavlik
2014	—	`conf`	Corleone: hands-off crowdsourcing for entity matching. SIGMOD Conference Chaitanya Gokhale, Sanjib Das, AnHai Doan, Jeffrey F. Naughton, Narasimhan Rampalli, Jude W. Shavlik, Xiaojin Zhu
2014	—	`conf`	Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM. SMIR@SIGIR Xiujun Li, Wei Gao, Jude W. Shavlik
2014	B	`conf`	Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. ILP Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik
2014	A*	`conf`	Relational One-Class Classification: A Non-Parametric Approach. AAAI Tushar Khot, Sriraam Natarajan, Jude W. Shavlik
2014	—	`conf`	Support Vector Machines for Differential Prediction. ECML/PKDD (2) Finn Kuusisto, Vítor Santos Costa, Houssam Nassif, Elizabeth S. Burnside, David Page, Jude W. Shavlik
2013	—	`conf`	Bootstrapping Knowledge Base Acceleration. TREC Tushar Khot, Ce Zhang, Jude W. Shavlik, Sriraam Natarajan, Christopher Ré
2013	Misc	`conf`	Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside
2013	A*	`conf`	Guiding Autonomous Agents to Better Behaviors through Human Advice. ICDM Gautam Kunapuli, Phillip Odom, Jude W. Shavlik, Sriraam Natarajan
2013	—	`conf`	Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude W. Shavlik, Vítor Santos Costa
2013	—	`conf`	Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik
2013	—	`conf`	Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text. StarAI@AAAI Sriraam Natarajan, Jose Picado, Tushar Khot, Kristian Kersting, Christopher Ré, Jude W. Shavlik
2013	C	`conf`	Using machine learning to identify benign cases with non-definitive biopsy. Healthcom Finn Kuusisto, Inês de Castro Dutra, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude W. Shavlik, Elizabeth S. Burnside
2012	—	`conf`	Big Data versus the Crowd: Looking for Relationships in All the Right Places. ACL (1) Ce Zhang, Feng Niu, Christopher Ré, Jude W. Shavlik
2012	—	`conf`	DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. VLDS Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik
2012	J	`jnl`	Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference. Int. J. Semantic Web Inf. Syst. Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik
2012	J	`jnl`	Gradient-based boosting for statistical relational learning: The relational dependency network case. Mach. Learn. Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik
2012	—	`conf`	Invited Talks. AAAI Fall Symposium: Discovery Informatics Timothy W. Clark, William W. Cohen, Lawrence Hunter, Chris J. Lintott, Jude W. Shavlik
2012	—	`conf`	Learning Relational Structure for Temporal Relation Extraction. StarAI@UAI Tushar Khot, Siddharth Srivastava, Sriraam Natarajan, Jude W. Shavlik
2012	—	`conf`	Mirror Descent for Metric Learning: A Unified Approach. ECML/PKDD (1) Gautam Kunapuli, Jude W. Shavlik
2012	J	`jnl`	Probabilistic Ensembles for Improved Inference in protein-Structure Determination. J. Bioinform. Comput. Biol. Ameet Soni, Jude W. Shavlik
2012	A*	`conf`	Scaling Inference for Markov Logic via Dual Decomposition. ICDM Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik
2012	—	`conf`	Twenty-Five Years of Combining Symbolic and Numeric Learning. NeSy@AAAI Jude W. Shavlik
2011	—	`conf`	Advice Refinement in Knowledge-Based SVMs. NIPS Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
2011	J	`jnl`	Felix: Scaling Inference for Markov Logic with an Operator-based Approach CoRR Feng Niu, Ce Zhang, Christopher Ré, Jude W. Shavlik
2011	A*	`conf`	Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. IJCAI Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2011	B	`conf`	Integrating knowledge capture and supervised learning through a human-computer interface. K-CAP Trevor Walker, Gautam Kunapuli, Noah Larsen, David Page, Jude W. Shavlik
2011	A*	`conf`	Learning Markov Logic Networks via Functional Gradient Boosting. ICDM Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik
2011	J	`jnl`	Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report. SIGHIT Rec. Faisal Farooq, Balaji Krishnapuram, Rómer Rosales, Shipeng Yu, Jude W. Shavlik, Raju Kucherlapati
2011	—	`conf`	Probabilistic ensembles for improved inference in protein-structure determination. BCB Ameet Soni, Jude W. Shavlik
2011	J	`jnl`	Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS CoRR Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik
2011	J	`jnl`	Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. Proc. VLDB Endow. Feng Niu, Christopher Ré, AnHai Doan, Jude W. Shavlik
2010	B	`conf`	Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. ILP Trevor Walker, Ciaran O'Reilly, Gautam Kunapuli, Sriraam Natarajan, Richard Maclin, David Page, Jude W. Shavlik
2010	—	`conf`	Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. StarAI@AAAI Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	—	`conf`	Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	—	`conf`	Guiding belief propagation using domain knowledge for protein-structure determination. BCB Ameet Soni, Craig A. Bingman, Jude W. Shavlik
2010	C	`conf`	Multi-Agent Inverse Reinforcement Learning. ICMLA Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
2010	—	`conf`	Online Knowledge-Based Support Vector Machines. ECML/PKDD (2) Gautam Kunapuli, Kristin P. Bennett, Amina Shabbeer, Richard Maclin, Jude W. Shavlik
2010	—	`ch.`	Transfer Learning via Advice Taking. Advances in Machine Learning I Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
2010	—	`conf`	Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside
2010	J	`jnl`	Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. J. Digit. Imaging Ryan W. Woods, Louis Oliphant, Kazuhiko Shinki, David Page, Jude W. Shavlik, Elizabeth S. Burnside
2009	—	`conf`	Anytime Lifted Belief Propagation. MLG/SRL@ILP Rodrigo de Salvo Braz, Sriraam Natarajan, Hung Bui, Jude W. Shavlik, Stuart Russell
2009	J	`jnl`	Bellwether analysis: Searching for cost-effective query-defined predictors in large databases. ACM Trans. Knowl. Discov. Data Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma
2009	B	`conf`	Boosting First-Order Clauses for Large, Skewed Data Sets. ILP Louis Oliphant, Elizabeth S. Burnside, Jude W. Shavlik
2009	—	`conf`	Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page
2009	—	`conf`	Knowledge Intensive Learning - Directed vs Undirected SRL Models. MLG/SRL@ILP Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik
2009	C	`conf`	Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. ICMLA Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapuli, Jude W. Shavlik
2009	B	`conf`	Policy Transfer via Markov Logic Networks. ILP Lisa Torrey, Jude W. Shavlik
2009	A*	`conf`	Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. IJCAI Jude W. Shavlik, Sriraam Natarajan
2009	J	`jnl`	Spherical-harmonic decomposition for molecular recognition in electron-density maps. Int. J. Data Min. Bioinform. Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik
2008	—	`conf`	Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning. AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence Lisa Torrey, Trevor Walker, Richard Maclin, Jude W. Shavlik
2008	J	`jnl`	Guest editors' introduction: special issue on inductive logic programming (ILP-2007). Mach. Learn. Hendrik Blockeel, Jude W. Shavlik, Prasad Tadepalli
2008	B	`ed.`	Inductive Logic Programming, 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers ILP Hendrik Blockeel, Jan Ramon, Jude W. Shavlik, Prasad Tadepalli
2008	—	`ch.`	Rule Extraction for Transfer Learning. Rule Extraction from Support Vector Machines Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
2007	B	`conf`	Building Relational World Models for Reinforcement Learning. ILP Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richard Maclin
2007	B	`conf`	Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. ILP Mark H. Goadrich, Jude W. Shavlik
2007	J	`jnl`	Creating protein models from electron-density maps using particle-filtering methods. Bioinform. Frank DiMaio, Dmitry A. Kondrashov, Eduard Bitto, Ameet Soni, Craig A. Bingman, George N. Phillips Jr., Jude W. Shavlik
2007	—	`conf`	Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics. BIBM Frank DiMaio, Ameet Soni, George N. Phillips, Jude W. Shavlik
2007	A*	`conf`	Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming. AAAI Richard Maclin, Edward W. Wild, Jude W. Shavlik, Lisa Torrey, Trevor Walker
2007	B	`conf`	Relational Macros for Transfer in Reinforcement Learning. ILP Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
2007	B	`conf`	Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. ILP Louis Oliphant, Jude W. Shavlik
2006	A*	`conf`	A Simple and Effective Method for Incorporating Advice into Kernel Methods. AAAI Richard Maclin, Jude W. Shavlik, Trevor Walker, Lisa Torrey
2006	—	`conf`	A probabilistic approach to protein backbone tracing in electron density maps. ISMB (Supplement of Bioinformatics) Frank DiMaio, Jude W. Shavlik, George N. Phillips
2006	A*	`conf`	Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition. ICDM Frank DiMaio, Jude W. Shavlik
2006	A*	`conf`	Bellwether Analysis: Predicting Global Aggregates from Local Regions. VLDB Bee-Chung Chen, Raghu Ramakrishnan, Jude W. Shavlik, Pradeep Tamma
2006	J	`jnl`	Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves. Mach. Learn. Mark H. Goadrich, Louis Oliphant, Jude W. Shavlik
2006	—	`conf`	Skill Acquisition Via Transfer Learning and Advice Taking. ECML Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
2005	B	`conf`	A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment. ILP Héctor Corrada Bravo, David Page, Raghu Ramakrishnan, Jude W. Shavlik, Vítor Santos Costa
2005	A*	`conf`	Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression. AAAI Richard Maclin, Jude W. Shavlik, Lisa Torrey, Trevor Walker, Edward W. Wild
2005	B	`conf`	Knowledge transfer via advice taking. K-CAP Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin
2005	—	`conf`	Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. ECML Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richard Maclin
2005	A*	`conf`	View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik
2004	—	`conf`	A Self-Tuning Method for One-Chip SNP Identification. CSB Michael Molla, Jude W. Shavlik, Thomas Albert, Todd Richmond, Steven Smith
2004	J	`jnl`	Knowledge-Based Kernel Approximation. J. Mach. Learn. Res. Olvi L. Mangasarian, Jude W. Shavlik, Edward W. Wild
2004	B	`conf`	Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. ILP Mark H. Goadrich, Louis Oliphant, Jude W. Shavlik
2004	B	`conf`	Learning an Approximation to Inductive Logic Programming Clause Evaluation. ILP Frank DiMaio, Jude W. Shavlik
2004	—	`conf`	Pictorial Structures for Molecular Modeling: Interpreting Density Maps. NIPS Frank DiMaio, Jude W. Shavlik, George N. Phillips
2004	B	`conf`	Scaling Up ILP: Experiences with Extracting Relations from Biomedical Text. ILP Jude W. Shavlik
2004	A*	`conf`	Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. KDD Jude W. Shavlik, Mark Shavlik
2004	J	`jnl`	Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Mag. Michael Molla, Michael Waddell, David Page, Jude W. Shavlik
2003	J	`jnl`	A Bayesian Network Approach to Operon Prediction. Bioinform. Joseph Bockhorst, Mark W. Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner
2003	J	`jnl`	A System for Building Intelligent Agents that Learn to Retrieve and Extract Information. User Model. User Adapt. Interact. Tina Eliassi-Rad, Jude W. Shavlik
2003	B	`conf`	Applying Theory Revision to the Design of Distributed Databases. ILP Fernanda Araújo Baião, Marta Mattoso, Jude W. Shavlik, Gerson Zaverucha
2003	—	`ch.`	Intelligent Web Agents that Learn to Retrieve and Extract Information. Intelligent Exploration of the Web Tina Eliassi-Rad, Jude W. Shavlik
2003	A*	`conf`	Knowledge-Based Nonlinear Kernel Classifiers. COLT Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik
2003	—	`conf`	Toward Automatic Management of Embarrassingly Parallel Applications. Euro-Par Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik, Michael Waddell
2002	B	`conf`	An Empirical Evaluation of Bagging in Inductive Logic Programming. ILP Inês de Castro Dutra, David Page, Vítor Santos Costa, Jude W. Shavlik
2002	A	`conf`	Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. ISMB J. B. Tobler, Michael Molla, Emile F. Nuwaysir, R. D. Green, Jude W. Shavlik
2002	—	`conf`	Interpreting Microarray Expression Data Using Text Annotating the Genes. JCIS Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik
2002	J	`jnl`	Interpreting microarray expression data using text annotating the genes. Inf. Sci. Michael Molla, Peter Andreae, Jeremy D. Glasner, Frederick R. Blattner, Jude W. Shavlik
2002	—	`conf`	Knowledge-Based Support Vector Machine Classifiers. NIPS Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik
2002	J	`jnl`	Report on the First International Conference on Knowledge Capture (K-CAP). AI Mag. Yolanda Gil, Mark A. Musen, Jude W. Shavlik
2001	A*	`conf`	A Theory-Refinement Approach to Information Extraction. ICML Tina Eliassi-Rad, Jude W. Shavlik
2000	A	`conf`	A Probabilistic Learning Approach to Whole-Genome Operon Prediction. ISMB Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner
2000	A	`conf`	Learning users' interests by unobtrusively observing their normal behavior. IUI Jeremy Goecks, Jude W. Shavlik
2000	A*	`conf`	Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. ICML Mark W. Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner
1999	A	`conf`	An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web. IUI Jude W. Shavlik, Susan Calcari, Tina Eliassi-Rad, Jack Solock
1999	A	`conf`	Bridging Science and Applications (Panel). IUI Jude W. Shavlik, Lawrence Birnbaum, William R. Swartout, Eric Horvitz, Barbara Hayes-Roth
1999	J	`jnl`	Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies. Bioinform. Carolyn F. Allex, Jude W. Shavlik, Frederick R. Blattner
1998	—	`ch.`	Creating Advice-Taking Reinforcement Learners. Learning to Learn Richard Maclin, Jude W. Shavlik
1998	A*	`ed.`	Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998 ICML Jude W. Shavlik
1997	J	`jnl`	Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies CoRR David W. Opitz, Jude W. Shavlik
1997	J	`jnl`	Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. J. Artif. Intell. Res. David W. Opitz, Jude W. Shavlik
1997	A	`conf`	Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations. ISMB Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner
1997	J	`jnl`	Understanding Time-Series Networks: A Case Study in Rule Extraction. Int. J. Neural Syst. Mark W. Craven, Jude W. Shavlik
1997	J	`jnl`	Using neural networks for data mining. Future Gener. Comput. Syst. Mark W. Craven, Jude W. Shavlik
1997	—	`conf`	Using neural networks to automatically refine expert system knowledge bases: experiments in the NYNEX MAX domain. ICNN David W. Opitz, Mark W. Craven, Jude W. Shavlik
1996	J	`jnl`	Actively Searching for an Effective Neural Network Ensemble. Connect. Sci. David W. Opitz, Jude W. Shavlik
1996	J	`jnl`	Creating Advice-Taking Reinforcement Learners. Mach. Learn. Richard Maclin, Jude W. Shavlik
1996	A*	`conf`	Growing Simpler Decision Trees to Facilitate Knowledge Discovery. KDD Kevin J. Cherkauer, Jude W. Shavlik
1996	A	`conf`	Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications. ISMB Carolyn F. Allex, Schuyler F. Baldwin, Jude W. Shavlik, Frederick R. Blattner
1995	A*	`conf`	Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks. IJCAI Richard Maclin, Jude W. Shavlik
1995	J	`jnl`	Dynamically adding symbolically meaningful nodes to knowledge-based neural networks. Knowl. Based Syst. David W. Opitz, Jude W. Shavlik
1995	—	`conf`	Extracting Tree-Structured Representations of Trained Networks. NIPS Mark W. Craven, Jude W. Shavlik
1995	—	`conf`	Generating Accurate and Diverse Members of a Neural-Network Ensemble. NIPS David W. Opitz, Jude W. Shavlik
1995	J	`jnl`	Introduction. Mach. Learn. Jude W. Shavlik, Lawrence Hunter, David B. Searls
1995	—	`conf`	Rapid Quality Estimation of Neural Network Input Representations. NIPS Kevin J. Cherkauer, Jude W. Shavlik
1994	J	`jnl`	Combining Symbolic and Neural Learning. Mach. Learn. Jude W. Shavlik
1994	A*	`conf`	Incorporating Advice into Agents that Learn from Reinforcements. AAAI Richard Maclin, Jude W. Shavlik
1994	J	`jnl`	Knowledge-Based Artificial Neural Networks. Artif. Intell. Geoffrey G. Towell, Jude W. Shavlik
1994	J	`jnl`	Machine Learning Approaches to Gene Recognition. IEEE Expert Mark W. Craven, Jude W. Shavlik
1994	J	`jnl`	The First International Conference on Intelligent Systems for Molecular Biology. AI Mag. David B. Searls, Jude W. Shavlik, Lawrence Hunter
1994	A*	`conf`	Using Genetic Search to Refine Knowledge-based Neural Networks. ICML David W. Opitz, Jude W. Shavlik
1994	A*	`conf`	Using Sampling and Queries to Extract Rules from Trained Neural Networks. ICML Mark W. Craven, Jude W. Shavlik
1993	J	`jnl`	Extracting Refined Rules from Knowledge-Based Neural Networks. Mach. Learn. Geoffrey G. Towell, Jude W. Shavlik
1993	A*	`conf`	Heuristically Expanding Knowledge-Based Neural Networks. IJCAI David W. Opitz, Jude W. Shavlik
1993	A*	`conf`	Learning Symbolic Rules Using Artificial Neural Networks. ICML Mark W. Craven, Jude W. Shavlik
1993	A*	`conf`	Learning to Represent Codons: A Challenge Problem for Constructive Induction. IJCAI Mark W. Craven, Jude W. Shavlik
1993	A	`ed.`	Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology, Bethesda, MD, USA, July 1993 ISMB Lawrence Hunter, David B. Searls, Jude W. Shavlik
1993	A	`conf`	Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools. ISMB Kevin J. Cherkauer, Jude W. Shavlik
1993	J	`jnl`	Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Mach. Learn. Richard Maclin, Jude W. Shavlik
1992	J	`jnl`	Refining PID Controllers Using Neural Networks. Neural Comput. Gary M. Scott, Jude W. Shavlik, W. Harmon Ray
1992	A*	`conf`	Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. AAAI Richard Maclin, Jude W. Shavlik
1992	A*	`conf`	Using Symbolic Learning to Improve Knowledge-Based Neural Networks. AAAI Geoffrey G. Towell, Jude W. Shavlik
1992	J	`jnl`	Visualizing Learning and Computation in Artificial Neural Networks. Int. J. Artif. Intell. Tools Mark W. Craven, Jude W. Shavlik
1991	—	`conf`	Constructive Induction in Knowledge-Based Neural Networks. ML Geoffrey G. Towell, Mark W. Craven, Jude W. Shavlik
1991	—	`conf`	Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules. NIPS Geoffrey G. Towell, Jude W. Shavlik
1991	—	`conf`	Refined PID Controllers Using Neural Networks. NIPS Gary M. Scott, Jude W. Shavlik, W. Harmon Ray
1991	—	`conf`	Refining Domain Theories Expressed as Finite-State Automata. ML Richard Maclin, Jude W. Shavlik
1991	J	`jnl`	Symbolic and Neural Learning Algorithms: An Experimental Comparison. Mach. Learn. Jude W. Shavlik, Raymond J. Mooney, Geoffrey G. Towell
1990	J	`jnl`	Acquiring Recursive and Iterative Concepts with Explanation-Based Learning. Mach. Learn. Jude W. Shavlik
1990	J	`jnl`	Learning in Mathematically-Based Domains: Understanding and Generalizing Obstacle Cancellations. Artif. Intell. Jude W. Shavlik, Gerald DeJong
1990	A*	`conf`	Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks. AAAI Geoffrey G. Towell, Jude W. Shavlik, Michiel O. Noordewier
1990	—	`conf`	Training Knowledge-Based Neural Networks to Recognize Genes. NIPS Michiel O. Noordewier, Geoffrey G. Towell, Jude W. Shavlik
1989	A*	`conf`	Acquiring Recursive Concepts with Explanation-Based Learning. IJCAI Jude W. Shavlik
1989	—	`conf`	An Empirical Analysis of EBL Approaches for Learning Plan Schemata. ML Jude W. Shavlik
1989	A*	`conf`	An Experimental Comparison of Symbolic and Connectionist Learning Algorithms. IJCAI Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, Alan Gove
1989	—	`conf`	Combining Explanation-Based Learning and Artificial Neural Networks. ML Jude W. Shavlik, Geoffrey G. Towell
1989	—	`conf`	Enriching Vocabularies by Generalizing Explanation Structures. ML Richard Maclin, Jude W. Shavlik
1989	—	`conf`	Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell
1988	—	`—`	Generalizing the Structure of Explanations in Explanation-Based Learning Jude W. Shavlik
1987	A*	`conf`	An Explanation-based Approach to Generalizing Number. IJCAI Jude W. Shavlik, Gerald DeJong
1987	A*	`conf`	BAGGER: An EBL System that Extends and Generalizes Explanations. AAAI Jude W. Shavlik, Gerald DeJong
1986	—	`conf`	Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving. SYMSAC Jude W. Shavlik, Gerald DeJong
1985	A*	`conf`	Learning about Momentum Conservation. IJCAI Jude W. Shavlik