Jude W. Shavlik

Jude W. Shavlik, Yirong Wu, Elizabeth S. Burnside, 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

Relational One-Class Classification: A Non-Parametric Approach

Sriraam Natarajan, Jude W. Shavlik, Tushar Khot, 2014, AAAI.

Case Study 3 — PHYSICS 101: Learning in Mathematically Based Domains

Jude W. Shavlik, 1993 .

A Bayesian Network Approach to Operon Prediction

David Page, Jude W. Shavlik, Mark W. Craven, 2003, Bioinform..

A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment

Raghu Ramakrishnan, David Page, Vítor Santos Costa, 2005, ILP.

Constructive Induction in Knowledge-Based Neural Networks

Jude W. Shavlik, Mark W. Craven, Geoffrey G. Towell, 1991, ML.

Knowledge-Based Nonlinear Kernel Classifiers

Glenn Fung, Jude W. Shavlik, Olvi L. Mangasarian, 2003, COLT.

Using Genetic Search to Refine Knowledge-based Neural Networks

David W. Opitz, Jude W. Shavlik, J. Shavlik, 1994, ICML.

Bellwether analysis: Searching for cost-effective query-defined predictors in large databases

Raghu Ramakrishnan, Jude W. Shavlik, Bee-Chung Chen, 2009, TKDD.

Symbolic and neural learning algorithms: An experimental comparison

Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, 1991, Machine Learning.

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming

David Page, Jude W. Shavlik, Mehmet Ayvaci, 2010, IHI.

Knowledge-Based Support Vector Machine Classifiers

Glenn Fung, Jude W. Shavlik, Olvi L. Mangasarian, 2002, NIPS.

Using Sampling and Queries to Extract Rules from Trained Neural Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1994, ICML.

Boosted Statistical Relational Learners

Kristian Kersting, Sriraam Natarajan, Jude W. Shavlik, 2014, SpringerBriefs in Computer Science.

A self-tuning method for one-chip SNP identification

Jude W. Shavlik, Todd Richmond, Steven Smith, 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

Guiding belief propagation using domain knowledge for protein-structure determination

Jude W. Shavlik, Ameet Soni, Craig A. Bingman, 2010, BCB '10.

Guiding Autonomous Agents to Better Behaviors through Human Advice

Sriraam Natarajan, Jude W. Shavlik, Gautam Kunapuli, 2013, 2013 IEEE 13th International Conference on Data Mining.

Probabilistic ensembles for improved inference in protein-structure determination

Jude W. Shavlik, Ameet Soni, J. Shavlik, 2011, BCB '11.

Rapid Quality Estimation of Neural Network Input Representations

Jude W. Shavlik, Kevin J. Cherkauer, J. Shavlik, 1995, NIPS.

Learning about Momentum Conservation

Jude W. Shavlik, J. Shavlik, 1985, IJCAI.

Big Data versus the Crowd: Looking for Relationships in All the Right Places

Christopher Ré, Feng Niu, Jude W. Shavlik, 2012, ACL.

Refinement ofApproximate Domain Theories by Knowledge-Based Neural Networks

Jude W. Shavlik, Geoffrey G. Towell, Michiel O. Noordewier, 1990, AAAI.

Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules

Jude W. Shavlik, Geoffrey G. Towell, J. Shavlik, 1991, NIPS.

Using I llou

Jude W. Shavlik, 1992 .

Refining Domain Theories Expressed as Finite-State Automata

Jude W. Shavlik, Richard Maclin, J. Shavlik, 1991, ML.

Pictorial Structures for Molecular Modeling: Interpreting Density Maps

Jude W. Shavlik, Frank DiMaio, George N. Phillips, 2004, NIPS.

Dynamically adding symbolically meaningful nodes to knowledge-based neural networks

David W. Opitz, Jude W. Shavlik, J. Shavlik, 1995, Knowl. Based Syst..

Refining PID Controllers Using Neural Networks

Jude W. Shavlik, W. Harmon Ray, Gary M. Scott, 1991, Neural Computation.

Combining Explanation-Based Learning and Artificial Neural Networks

Jude W. Shavlik, Geoffrey G. Towell, 1989, ML.

Skill Acquisition Via Transfer Learning and Advice Taking

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2006, ECML.

Growing Simpler Decision Trees to Facilitate Knowledge Discovery

Jude W. Shavlik, Kevin J. Cherkauer, J. Shavlik, 1996, KDD.

Learning by symbolic and neural methods

Jude W. Shavlik, 1998 .

Acquiring Recursive Concepts with Explanation-Based Learning

Jude W. Shavlik, J. Shavlik, 1989, IJCAI.

Learning Relational Dependency Networks for Relation Extraction pdf

Sriraam Natarajan, Jude W. Shavlik, Ameet Soni, 2016, ILP.

Combining Symbolic and Neural Learning

Jude W. Shavlik, J. Shavlik, 1994, Machine Learning.

in Advances in Neural Information Processing

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1996 .

Incorporating Advice into Agents that Learn from Reinforcements

Jude W. Shavlik, Richard Maclin, J. Shavlik, 1994, AAAI.

An Experimental Comparison of Symbolic and Connectionist Learning Algorithms

Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, 1989, IJCAI.

Acquiring recursive and iterative concepts with explanation-based learning

Jude W. Shavlik, J. Shavlik, 2004, Machine Learning.

Advice Refinement in Knowledge-Based SVMs

Jude W. Shavlik, Gautam Kunapuli, Richard Maclin, 2011, NIPS.

Using Machine Learning to Design and Interpret Gene-Expression Microarrays

David Page, Jude W. Shavlik, Michael Waddell, 2004, AI Mag..

Actively Searching for an Effective Neural Network Ensemble

David W. Opitz, Jude W. Shavlik, J. Shavlik, 1996, Connect. Sci..

Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2005, ECML.

Classification from One Class of Examples for Relational Domains

Sriraam Natarajan, Jude W. Shavlik, Tushar Khot, 2014, StarAI@AAAI.

Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2008, AAAI Fall Symposium: Naturally-Inspired Artificial Intelligence.

Felix: Scaling Inference for Markov Logic with an Operator-based Approach pdf

Christopher Ré, Feng Niu, Jude W. Shavlik, 2011, ArXiv.

Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming

Jude W. Shavlik, Louis Oliphant, 2007, ILP.

Learning users' interests by unobtrusively observing their normal behavior

Jude W. Shavlik, Jeremy Goecks, J. Shavlik, 2000, IUI '00.

Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM

Wei Gao, Xiujun Li, Jude W. Shavlik, 2014, SMIR@SIGIR.

Invited Talks

Lawrence Hunter, William W. Cohen, Jude W. Shavlik, 2019, AAAI Fall Symposium: Discovery Informatics.

Introduction

Lawrence Hunter, Jude W. Shavlik, David B. Searls, 2004, Machine Learning.

Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools

Jude W. Shavlik, Kevin J. Cherkauer, J. Shavlik, 1993, ISMB.

Machine learning approaches to gene recognition

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1994, IEEE Expert.

Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge

Ciaran O'Reilly, Sriraam Natarajan, David Page, 2010, ILP.

Bellwether analysis: predicting global aggregates from local regions

Raghu Ramakrishnan, Jude W. Shavlik, Bee-Chung Chen, 2006, VLDB.

Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network

Sriraam Natarajan, Jude W. Shavlik, 2009, IJCAI.

Visualizing Learning and Computation in Artificial Neural Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1992, Int. J. Artif. Intell. Tools.

Training Knowledge-Based Neural Networks to Recognize Genes

Jude W. Shavlik, Geoffrey G. Towell, Michiel O. Noordewier, 1990, NIPS.

Knowledge transfer via advice taking

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2005, K-CAP '05.

Toward Automatic Management of Embarrassingly Parallel Applications

David Page, Vítor Santos Costa, Jude W. Shavlik, 2003, Euro-Par.

Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations

Jude W. Shavlik, Frederick R. Blattner, Carolyn F. Allex, 1997, ISMB.

Creating protein models from electron-density maps using particle-filtering methods

Jude W. Shavlik, George N. Phillips, Frank DiMaio, 2007, Bioinform..

Evaluating machine learning approaches for aiding probe selection for gene-expression arrays

Jude W. Shavlik, Michael Molla, J. B. Tobler, 2002, ISMB.

Multi-Agent Inverse Reinforcement Learning

Kristian Kersting, Sriraam Natarajan, Prasad Tadepalli, 2010, 2010 Ninth International Conference on Machine Learning and Applications.

Computer understanding and generalization of symbolic mathematical calculations: a case study in physics problem solving

Gerald DeJong, Jude W. Shavlik, J. Shavlik, 1986, SYMSAC '86.

Learning Markov Logic Networks via Functional Gradient Boosting

Kristian Kersting, Sriraam Natarajan, Jude W. Shavlik, 2011, 2011 IEEE 11th International Conference on Data Mining.

A System for Building Intelligent Agents that Learn to Retrieve and Extract Information

Tina Eliassi-Rad, Jude W. Shavlik, J. Shavlik, 2003, User Modeling and User-Adapted Interaction.

Anomaly Detection in Text: The Value of Domain Knowledge

Sriraam Natarajan, Jude W. Shavlik, Tushar Khot, 2015, FLAIRS.

Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS pdf

Christopher Ré, Feng Niu, Jude W. Shavlik, 2011, Proc. VLDB Endow..

A Theory-Refinement Approach to Information Extraction

Tina Eliassi-Rad, Jude W. Shavlik, J. Shavlik, 2001, ICML.

Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2005, AAAI.

Using neural networks for data mining

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1997, Future Gener. Comput. Syst..

Bootstrapping Knowledge Base Acceleration

Christopher Ré, Sriraam Natarajan, Jude W. Shavlik, 2013, TREC.

Proceedings, Third IEEE International Conference on Data Mining, ICDM 2003, 19-22 November 2003, Melbourne, Florida

Xindong Wu, Jude W. Shavlik, Alex Tuzhilin, 2003 .

Symbolic and Neural Learning Algorithms: An Experimental Comparison

Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell, 1991, Machine Learning.

Relational Macros for Transfer in Reinforcement Learning

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2007, ILP.

View Learning for Statistical Relational Learning: With an Application to Mammography

Jesse Davis, Raghu Ramakrishnan, David Page, 2005, IJCAI.

Support Vector Machines for Differential Prediction

David Page, Vítor Santos Costa, Jude W. Shavlik, 2014, ECML/PKDD.

Boosting First-Order Clauses for Large, Skewed Data Sets

Jude W. Shavlik, Elizabeth S. Burnside, Louis Oliphant, 2009, ILP.

TAC KBP 2015 : English Slot Filling Track Relational Learning with Expert Advice

Sriraam Natarajan, Jude W. Shavlik, Ameet Soni, 2015, TAC.

Knowledge-Based Kernel Approximation

Jude W. Shavlik, Olvi L. Mangasarian, Edward W. Wild, 2004, J. Mach. Learn. Res..

Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2007, AAAI.

Bridging science and applications (panel 1)

Eric Horvitz, Lawrence Birnbaum, Jude W. Shavlik, 1998, IUI '99.

Understanding Time-Series Networks: A Case Study in Rule Extraction

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1997, Int. J. Neural Syst..

Learning an Approximation to Inductive Logic Programming Clause Evaluation

Jude W. Shavlik, Frank DiMaio, J. Shavlik, 2004, ILP.

Rule Extraction for Transfer Learning

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2008, Rule Extraction from Support Vector Machines.

Information Extraction for Clinical Data Mining: A Mammography Case Study

David Page, Jude W. Shavlik, Mehmet Ayvaci, 2009, 2009 IEEE International Conference on Data Mining Workshops.

The First International Conference on Intelligent Systems for Molecular Biology

Lawrence Hunter, David B. Searls, Jude W. Shavlik, 1994, AI Mag..

Guest editors’ introduction: special issue on inductive logic programming (ILP-2007)

Hendrik Blockeel, Prasad Tadepalli, Jude W. Shavlik, 2008, Machine Learning.

Intelligent Web Agents that Learn to Retrieve and Extract Information

Tina Eliassi-Rad, Jude W. Shavlik, J. Shavlik, 2003, Intelligent Exploration of the Web.

Predictive Models in Personalized Medicine: Neural Information Processing Systems (NIPS), 2010 workshop report

Rómer Rosales, Shipeng Yu, Jude W. Shavlik, 2011, SIGH.

Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage

Jude W. Shavlik, Mark Shavlik, J. Shavlik, 2004, KDD.

Knowledge-Based Artificial Neural Networks

Jude W. Shavlik, Geoffrey G. Towell, J. Shavlik, 1994, Artif. Intell..

Corleone: hands-off crowdsourcing for entity matching

Jeffrey F. Naughton, Chaitanya S. Gokhale, Xiaojin Zhu, 2014, SIGMOD Conference.

Spherical-harmonic decomposition for molecular recognition in electron-density maps

Jude W. Shavlik, Frank DiMaio, George N. Phillips, 2009, Int. J. Data Min. Bioinform..

Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies

Jude W. Shavlik, Frederick R. Blattner, Carolyn F. Allex, 1999, Bioinform..

An Empirical Evaluation of Bagging in Inductive Logic Programming

David Page, Vítor Santos Costa, Jude W. Shavlik, 2002, ILP.

Transfer Learning via Advice Taking

Jude W. Shavlik, Richard Maclin, Trevor Walker, 2010, Advances in Machine Learning I.

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

David Page, Jude W. Shavlik, Elizabeth S. Burnside, 2009, Journal of Digital Imaging.

Creating advice-taking reinforcement learners

Jude W. Shavlik, Richard Maclin, J. Shavlik, 2004, Machine Learning.

An Explanation-based Approach to Generalizing Number

Gerald DeJong, Jude W. Shavlik, J. Shavlik, 1987, IJCAI.

Gradient-based boosting for statistical relational learning: The relational dependency network case

Kristian Kersting, Sriraam Natarajan, Jude W. Shavlik, 2011, Machine Learning.

A Recap of Early Work on Theory and Knowledge Refinement

Raymond J. Mooney, Jude W. Shavlik, J. Shavlik, 2021, AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering.

Case-Based Reasoning with Noisy Case Boundaries: An Application in Molecular Biology

Jude W. Shavlik, J. Shavlik, 1990 .

Extracting Thee-Structured Representations of Thained Networks

Jude W. Shavlik, Mark W. Craven, J. Shavlik, 1995 .