Publications of the UW ML Research Group

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This page contains relevant information about publications by the members of Prof. Shavlik's Machine Learning Research Group at the University of Wisconsin - Madison.

Add your publication (Shavlik Group members and collaborators)

Journal Articles

T. Khot, S. Natarajan, K. Kersting & J. Shavlik (2014).
Gradient-based Boosting for Statistical Relational Learning: The Markov Logic Network and Missing Data Cases. Machine Learning.
(To appear.)
Abstract.
F. Niu, C. Zhang, C. Re & J. Shavlik (2012).
Elementary: Large-scale Knowledge-base Construction via Machine Learning and Statistical Inference. International Journal on Semantic Web and Information Systems (IJSWIS).
Abstract.
A. Soni & J. Shavlik (2012).
Probabilistic Ensembles for Improved Inference in Protein-Structure Determination. Journal of Bioinformatics and Computational Biology (JBCB). doi: http://dx.doi.org/10.1142/S0219720012400094
An extended version of this paper can be found in The Proceedings of the ACM International Conference on Bioinformatics and Computational Biology 2011.
Abstract.
S. Natarajan, T. Khot, K. Kersting, B. Gutmann & J. Shavlik (2012).
Gradient-based Boosting for Statistical Relational Learning: The Relational Dependency Network Case. Machine Learning. doi: http://dx.doi.org/10.1007/s10994-011-5244-9
Abstract.
T. Ayer, O. Alagoz, J. Chhatwal, J. Shavlik, C. Kahn & E. Burnside (2010).
Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration. Cancer. doi: http://dx.doi.org/10.1002/cncr.25081
Abstract.
R. Woods, L. Oliphant, K. Shinki, D. Page, J. Shavlik & E. Burnside (2010).
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. Journal of Digital Imaging, 23, pp. 554-561. doi: 10.1007/s10278-009-9235-3
Abstract.
B-C. Chen, R. Ramakrishnan, J. Shavlik & P. Tamma (2009).
Bellwether Analysis: Searching for Cost-Effective Query-Defined Predictors in Large Databases. ACM Transactions on Knowledge Discovery from Data, 3, pp. 1-49. doi: http://doi.acm.org/10.1145/1497577.1497582
Abstract.
F. DiMaio, A. Soni, G. Phillips & J. Shavlik (2009).
Spherical-Harmonic Decomposition for Molecular Recognition in Electron-Density Maps. International Journal of Data Mining and Bioinformatics, 3, pp. 205-227. doi: 10.1504/IJDMB.2009.024852 NIHMSID: NIHMS68171 PMCID: PMC2696052
(The paper is in pre-publication form. It is an extension to: DiMaio et al. (BIBM 2007))
Abstract.
D. Severtson, L. Pape, D. Page, J. Shavlik, G. Phillips & P. Flatley Brennan (2007).
Biomedical Informatics Training at the University of Wisconsin-Madison. Methods Inf Med., 46 (Suppl. 1), pp. 149-156.
(Full paper not available. PubMed ID 17700918)
Abstract.
F. DiMaio, D. Kondrashov, E. Bitto, A. Soni, C. Bingman, G. Phillips & J. Shavlik (2007).
Creating Protein Models from Electron-Density Maps using Particle-Filtering Methods. Bioinformatics, 23, pp. 2851-2858. doi: 10.1093/bioinformatics/btm480 PMCID: PMC2567142
Code. Data. Abstract.
F. DiMaio, J. Shavlik & G. Phillips (2006).
A Probabilistic Approach to Protein Backbone Tracing in Electron Density Maps. Bioinformatics, Special Issue Based on the Papers Presented at the Fourteenth International Conference on Intelligent Systems for Molecular Biology (ISMB-06), Fortaleza, Brazil, 22, pp. e81-e89.
Slides (PPT). Code. Data. Abstract.
M. Goadrich, L. Oliphant & J. Shavlik (2006).
Gleaner: Creating Ensembles of First-Order Clauses to Improve Recall-Precision Curves. Machine Learning, 64, pp. 231-262.
Data. Abstract.
O. Mangasarian, J. Shavlik & E. Wild (2004).
Knowledge-Based Kernel Approximation. Journal of Machine Learning Research, 5, pp. 1127-1141.
Abstract.
M. Molla, M. Waddell, D. Page & J. Shavlik (2004).
Using Machine Learning to Design and Interpret Gene-Expression Microarrays. AI Magazine, 25, pp. 23-44.
(To Appear in the Special Issue on Bioinformatics)
Abstract.
J. Bockhorst, M. Craven, D. Page, J. Shavlik & J. Glasner (2003).
A Bayesian Network Approach to Operon Prediction. Bioinformatics, 19, pp. 1227-1235.
Abstract.
T. Eliassi-Rad & J. Shavlik (2003).
A System for Building Intelligent Agents that Learn to Retrieve and Extract Information. International Journal on User Modeling and User-Adapted Interaction, Special Issue on User Modeling and Intelligent Agents, 13, pp. 35-88.
Abstract.
M. Molla, P. Andreae, J. Glasner, F. Blattner & J. Shavlik (2002).
Interpreting Microarray Expression Data Using Text Annotating the Genes. Information Sciences, 146, pp. 75-88.
Also appears in: Proceedings of the 4th Conference on Computational Biology and Genome Informatics, Durham, NC
Slides (PPT). Abstract.
J. Tobler, M. Molla, E. Nuwaysir, R. Green & J. Shavlik (2002).
Evaluating Machine Learning Approaches for Aiding Probe Selection for Gene-Expression Arrays. Bioinformatics, Special Issue Based on the Papers Presented at the Tenth International Conference on Intelligent Systems for Molecular Biology (ISMB-02), Edmonton, Canada, 18, pp. S164-S171.
Slides (PPT). Abstract.
C. Allex, J. Shavlik & F. Blattner (1999).
Neural Network Input Representations that Produce Accurate Consensus Sequences from DNA Fragment Assemblies. Bioinformatics, 15, pp. 723-728.
Abstract.
D. Opitz & J. Shavlik (1997).
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. Journal of Artificial Intelligence Research, 6, pp. 177-209.
Abstract.
M. Craven & J. Shavlik (1997).
Using Neural Networks for Data Mining. Future Generation Computer Systems, 13, pp. 211-229.
Abstract.
M. Craven & J. Shavlik (1997).
Understanding Time-Series Networks: A Case Study in Rule Extraction. International Journal of Neural Systems, 8, pp. 373-384.
Abstract.
D. Opitz & J. Shavlik (1996).
Actively Searching for an Effective Neural-Network Ensemble. Connection Science, 8, pp. 337-353.
Data. Abstract.
R. Maclin & J. Shavlik (1996).
Creating Advice-Taking Reinforcement Learners. Machine Learning, 22, pp. 251-281.
Abstract.
K. Cherkauer (1995).
Stuffing Mind into Computer: Knowledge and Learning for Intelligent Systems. Informatica, 19, pp. 501-511.
Abstract.
D. Opitz & J. Shavlik (1995).
Dynamically Adding Symbolically Meaningful Nodes to Knowledge-Based Neural Networks. Knowledge-Based Systems, 8, pp. 301-311.
Abstract.
G. Towell & J. Shavlik (1994).
Knowledge-Based Artificial Neural Networks. Artificial Intelligence, 70, pp. 119-165.
Abstract.
J. Shavlik (1994).
A Framework for Combining Symbolic and Neural Learning. Machine Learning, 14, pp. 321-331.
(The local files are an extended version of the journal article.)
Abstract.
G. Towell & J. Shavlik (1993).
The Extraction of Refined Rules from Knowledge-Based Neural Networks. Machine Learning, 13, pp. 71-101.
Abstract.
R. Maclin & J. Shavlik (1993).
Using Knowledge-based Neural Networks To Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Machine Learning, 11, pp. 195-215.
Abstract.
M. Craven & J. Shavlik (1993).
Machine Learning Approaches to Gene Recognition. IEEE Expert, 9.
(The on-line file is a variant of the journal article.)
Abstract.
G. Scott, J. Shavlik & W. Ray (1992).
Refining PID Controllers using Neural Networks. Neural Computation, 4, pp. 746-757.
Abstract.
M. Craven & J. Shavlik (1992).
Visualizing Learning and Computation in Artificial Neural Networks. International Journal on Artificial Intelligence Tools, 1, pp. 399-425.
Abstract.
J. Shavlik, R. Mooney & G. Towell (1991).
Symbolic and Neural Network Learning Algorithms: An Experimental Comparison. Machine Learning, 6, pp. 111-143.
Abstract.
J. Shavlik (1990).
Acquiring Recursive and Iterative Concepts with Explanation-Based Learning. Machine Learning, 5, pp. 39-70.
Abstract.
J. Shavlik & G. DeJong (1990).
Learning in Mathematically-Based Domains: Understanding and Generalizing Obstacle Cancellations. Artificial Intelligence, 45, pp. 1-45.
Abstract.
J. Shavlik & G. Towell (1989).
Combining Explanation-based and Neural Learning: An Algorithm and Empirical Results. Connection Science, 1, pp. 233-255.
Abstract.

Conference Papers

J. Weiss, F. Kuusisto, K. Boyd, J. Liu & D. Page (2015).
Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. Proceedings of the AMIA Annual Symposium.
Abstract.
F. Kuusisto, I. Dutra, M. Elezaby, E. Mendonca, J. Shavlik & E. Burnside (2015).
Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems. Proceedings of the AMIA Joint Summits on Translational Science.
Slides (PDF). Abstract.
S. Natarajan, J. Picado T. Khot, K. Kersting, C. Re & J. Shavlik (2014).
Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge. Proceedings of 24th International Conference on Inductive Logic Programming (ILP).
Abstract.
F. Kuusisto, V. Santos Costa, H. Nassif, E. Burnside, D. Page & J. Shavlik (2014).
Support Vector Machines for Differential Prediction. Proceedings of the European Conference on Machine Learning (ECML).
Poster (PDF). Slides (PDF). Code. Data. Abstract.
T. Khot, S. Natarajan & J. Shavlik (2014).
Relational One-Class Classification: A Non-Parametric Approach. Proceedings of the 28th AAAI Conference.
Abstract.
C. Gokhale, S. Das, A. Doan, J. Naughton, N. Rampali, J. Shavlik & X. Zhu (2014).
Corleone: Hands-Off Crowdsourcing for Entity Matching. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, Utah.
Abstract.
H. Nassif, F. Kuusisto, E. Burnside & J. Shavlik (2013).
Uplift Modeling with ROC: An SRL Case Study. Proceedings of the 23rd International Conference on Inductive Logic Programming, Rio de Janeiro, Brazil.
Abstract.
G. Kunapuli, P. Odom, J. Shavlik & S. Natarajan (2013).
Guiding Autonomous Agents to Better Behaviors through Human Advice. Proceedings of the 13th IEEE International Conference on Data Mining, Dallas, Texas.
Abstract.
F. Kuusisto, I. Dutra, H. Nassif, Y. Wu, M. Klein, H. Neuman, J. Shavlik & E. Burnside (2013).
Using Machine Learning to Identify Benign Cases with Non-Definitive Biopsy. Proceedings of IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom), Lisbon, Portugal.
Abstract.
J. Liu, D. Page, H. Nassif, J. Shavlik, P. Peissig, C. McCarty, A. Onitilo & E. Burnside (2013).
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. Proceedings of the American Medical Informatics Association Annual Symposium (AMIA). PMCID: PMC3900221
Abstract.
H. Nassif, F. Kuusisto, E. Burnside, D. Page, J. Shavlik & V. Santos Costa (2013).
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. Proceedings of the European Conference on Machine Learning (ECML).
Abstract.
F. Niu, C. Zhang, C. Re & J. Shavlik (2012).
Scaling Inference for Markov Logic via Dual Decomposition. Proceedings of the IEEE International Conference on Data Mining (ICDM).
Abstract.
G. Kunapuli & J. Shavlik (2012).
Mirror Descent for Metric Learning: A Unified Approach. Proceedings of the European Conference on Machine Learning (ECML).
Poster (PDF). Slides (PDF). Abstract.
C. Zhang, F. Niu, C. Re & J. Shavlik (2012).
Big Data versus the Crowd: Looking for Relationships in All the Right Places. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL).
Poster (PDF). Abstract.
I. Dutra, H. Nassif, D. Page, J. Shavlik, R. Strigel, Y. Wu, M. Elezabi & E. Burnside (2011).
Integrating Machine Learning and Physician Knowledge to Improve the Accuracy of Breast Biopsy. Proceedings of the American Medical Informatics Association Annual Symposium (AMIA).
Slides (PPTX). Abstract.
T. Khot, S. Natarajan, K. Kersting & J. Shavlik (2011).
Learning Markov Logic Networks via Functional Gradient Boosting. Proceedings of the IEEE International Conference on Data Mining (ICDM).
Slides (PPTX). Abstract.
G. Kunapuli, R. Maclin & J. Shavlik (2011).
Advice Refinement for Knowledge-Based Support Vector Machines. Proceedings of the Twenty-Fifth Conference on Neural Information Processing Systems (NIPS).
Poster (PDF). Abstract.
A. Soni & J. Shavlik (2011).
Probabilistic Ensembles for Improved Inference in Protein-Structure Determination. Proceedings of the ACM International Conference on Bioinformatics and Computational Biology (ACM-BCB), Chicago, Illinois.
Slides (PPTX). Abstract.
S. Natarajan, S. Joshi, P. Tadepalli, K. Kersting & J. Shavlik (2011).
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach. Proceedings of 22nd International Joint Conference on Artificial Intelligence (IJCAI).
Abstract.
T. Walker, G. Kunapuli, N. Larsen, D. Page & J. Shavlik (2011).
Integrating Knowledge Capture and Supervised Learning through a Human-Computer Interface. Proceedings of 6th International Conference on Knowledge Capture (KCAP), Banff, Canada.
Slides (PPTX). Abstract.
F. Niu, C. Re, A. Doan & J. Shavlik (2011).
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. Proceedings of the 37th International Conference on Very Large Data Bases (VLDB 2011), Seattle, Washington.
Slides (PPTX). Abstract.
G. Kunapuli, K. Bennett, R. Maclin & J. Shavlik (2010).
The Adviceptron: Giving Advice To The Perceptron. Proceedings of the Conference on Artificial Neural Networks In Engineering (ANNIE 2010), St. Louis, MO.
Slides (PDF). Abstract.
T. Walker, C. O'Reilly, G. Kunapuli, S. Natarajan, R. Maclin, D. Page & J. Shavlik (2010).
Automating the ILP Setup Task: Converting User Advice about Specific Examples into General Background Knowledge. Proceedings of the 20th International Conference on Inductive Logic Programming, Florence, Italy.
Abstract.
H. Nassif, D. Page, M. Ayvaci, J. Shavlik & E. Burnside (2010).
Uncovering Age-Specific Invasive and DCIS Breast Cancer Rules Using Inductive Logic Programming. Proceedings of the 1st ACM International Health Informatics Symposium (IHI '10), Arlington, VA.
Abstract.
S. Natarajan, G. Kunapuli amd K. Judah, P. Tadepalli, K. Kersting & J. Shavlik (2010).
Multi-Agent Inverse Reinforcement Learning. Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2010), Washington DC, USA.
Abstract.
A. Soni, C. Bingman & J. Shavlik (2010).
Guiding Belief Propagation using Domain Knowledge for Protein-Structure Determination. Proceedings of the ACM International Conference on Bioinformatics and Computational Biology (ACM-BCB), Niagara Falls, New York.
Slides (PPTX). Abstract.
G. Kunapuli, K. Bennett, A. Shabbeer, R. Maclin & J. Shavlik (2010).
Online Knowledge-Based Support Vector Machines. Proceedings of the European Conference on Machine Learning (ECML 2010), Barcelona, Spain.
Slides (PDF). Abstract.
S. Natarajan, T. Khot, D. Lowd, P. Tadepalli, K. Kersting & J. Shavlik (2010).
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. Proceedings of European Conference in Machine Learning (ECML 2010), Barcelona, Spain.
Abstract.
L. Oliphant, E. Burnside & J. Shavlik (2009).
Boosting First-Order Clauses for Large, Skewed Data Sets. Proceedings of the Nineteenth Conference on Inductive Logic Programming, Leuven, Belgium.
Abstract.
S. Natarajan, P. Tadepalli, G. Kunapuli & J. Shavlik (2009).
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. Proceedings of the International Conference on Machine Learning and Applications, Florida.
Slides (PPTX). Abstract.
L. Torrey & J. Shavlik (2009).
Policy Transfer via Markov Logic Networks. Proceedings of the Nineteenth Conference on Inductive Logic Programming, Leuven, Belgium.
Slides (PPTX). Abstract.
J.Shavlik & S. Natarajan (2009).
Speeding Up Inference in Markov Logic Networks by Preprocessing to Reduce the Size of the Resulting Grounded Network. Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence, Pasadena, California.
Slides (PPTX). Abstract.
T. Walker, L. Torrey, J. Shavlik & R. Maclin (2007).
Building Relational World Models for Reinforcement Learning. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
Poster (PDF). Abstract.
L. Oliphant & J. Shavlik (2007).
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
Poster (PS). Slides (PPT). Abstract.
F. DiMaio, A. Soni, G. Phillips & J. Shavlik (2007).
Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'07), Fremont, CA.
Code. Data. Abstract.
M. Goadrich & J. Shavlik (2007).
Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
Abstract.
L. Torrey, J. Shavlik, T. Walker & R. Maclin (2007).
Relational Macros for Transfer in Reinforcement Learning. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
Poster (PDF). Slides (PPT). Abstract.
R. Maclin, E. Wild, J. Shavlik, L. Torrey & T. Walker (2007).
Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming. Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI'07), Vancouver, British Columbia.
Slides (PPT). Abstract.
F. DiMaio & J. Shavlik (2006).
Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition. Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM'06), pp. 845-850, Hong Kong.
A longer version of this paper appears as UW Machine Learning Research Group Working Paper 06-1.
Slides (PPT). Code. Data. Abstract.
L. Torrey, J. Shavlik, T. Walker & R. Maclin (2006).
Skill Acquisition via Transfer Learning and Advice Taking. Proceedings of the Seventeenth European Conference on Machine Learning (ECML'06), pp. 425-436, Berlin, Germany.
Slides (PPT). Code. Abstract.
B. Chen, R. Ramakrishnan, J. Shavlik & P. Tamma (2006).
Bellwether Analysis: Predicting Global Aggregates from Local Regions. Proceedings of the Thirty-Second International Conference on Very Large Data Bases (VLDB'06), pp. 655-666, Seoul, Korea.
Abstract.
R. Maclin, J. Shavlik, T. Walker & L. Torrey (2006).
A Simple and Effective Method for Incorporating Advice into Kernel Methods. Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI'06), Boston, MA.
Slides (PPT). Abstract.
J. Davis & M. Goadrich (2006).
The Relationship Between Precision-Recall and ROC Curves. Proceedings of the Twenty-Third International Conference on Machine Learning (ICML'06), Pittsburgh, PA.
Abstract.
L. Torrey, T. Walker, J. Shavlik & R. Maclin (2005).
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another. Proceedings of the Sixteenth European Conference on Machine Learning (ECML'05), pp. 412-424, Porto, Portugal.
Slides (PPT). Abstract.
L. Torrey, T. Walker, J. Shavlik & R. Maclin (2005).
Knowledge Transfer Via Advice Taking. Proceedings of the Third International Conference on Knowledge Capture (KCAP'05), pp. 217-218, Banff, Canada.
Abstract.
H.C. Bravo, D. Page, R. Ramakrishnan, J. Shavlik & V. Santos Costa (2005).
A Framework for Set-Oriented Computation in Inductive Logic Programming and its Application in Generalizing Inverse Entailment. Proceedings of the Fifteenth International Conference on Inductive Logic Programming, pp. 69-86, Bonn, Germany.
Abstract.
R. Maclin, J. Shavlik, L. Torrey, T. Walker & E. Wild (2005).
Giving Advice about Preferred Actions to Reinforcement Learners Via Knowledge-Based Kernel Regression. Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI'05), pp. 819-824, Pittsburgh, PA.
Slides (PPT). Code. Abstract.
J. Davis, E. Burnside, I. Dutra, D. Page, R. Ramakrishnan, V. Santos Costa & J. Shavlik (2005).
View Learning for Statistical Relational Learning: With an Application to Mammography. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pp. 677-683, Edinburgh, Scotland.
Abstract.
F. DiMaio, J. Shavlik & G. Phillips (2005).
Pictorial Structures for Molecular Modeling: Interpreting Density Maps. Advances in Neural Information Processing Systems (NIPS) 17, pp. 369-376, Vancouver, Canada.
Poster (PDF). Abstract.
M. Goadrich, L. Oliphant & J. Shavlik (2004).
Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. Proceedings of the Fourteenth International Conference on Inductive Logic Programming, pp. 98-115, Porto, Portugal.
Slides (PPT). Data. Abstract.
F. DiMaio & J. Shavlik (2004).
Learning an Approximation to Inductive Logic Programming Clause Evaluation. Proceedings of the Fourteenth International Conference on Inductive Logic Programming, pp. 80-97, Porto, Portugal.
Slides (PPT). Abstract.
J. Shavlik & M. Shavlik (2004).
Selection, Combination, and Evaluation of Effective Software Sensors for Detecting Abnormal Computer Usage. Proceedings of the Tenth International Conference on Knowledge Discovery and Data Mining, pp. 276-285, Seattle, WA.
Slides (PPT). Abstract.
M. Molla, J. Shavlik, T. Albert, T. Richmond & S. Smith (2004).
A Self-Tuning Method for One-Chip SNP Identification. Proceedings of the IEEE Conference on Computational Systems Bioinformatics, pp. 69-79, Stanford, CA.
Slides (PPT). Abstract.
F. Baiao, M. Mattoso, J. Shavlik & G. Zaverucha (2003).
Applying Theory Revision to the Design of Distributed Databases. Proceedings of the Thirteenth International Conference on Inductive Logic Programming, pp. 57-74, Szeged, Hungary.
Abstract.
G. Fung, O. Mangasarian & J. Shavlik (2003).
Knowledge-Based Nonlinear Kernel Classifiers. 16th Annual Conference on Learning Theory (COLT) and 7th Annual Workshop on Kernel Machines, Proceedings, Washington, DC.
Abstract.
I. Dutra, D. Page, V. Santos Costa, J. Shavlik & M. Waddell (2003).
Toward Automatic Management of Embarrassingly Parallel Applications. Proceedings of International Conference on Parallel and Distributed Computing (Euro-Par), Klagenfurt, Austria.
Abstract.
G. Fung, O. Mangasarian & J. Shavlik (2002).
Knowledge-Based Support Vector Machine Classifiers. Proceedings of Sixteenth Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada.
Abstract.
I. Dutra, D. Page, V. Santos Costa & J. Shavlik (2002).
An Empirical Evaluation of Bagging in Inductive Logic Programming. Proceedings of the Twelfth International Conference on Inductive Logic Programming, pp. 48--65, Sydney, Australia.
Abstract.
J. Shavlik, M. Shavlik & M. Fahland (2001).
Evaluating Software Sensors for Actively Profiling Windows 2000 Computer Users. Presented at the Fourth International Symposium on Recent Advances in Intrusion Detection, Davis, CA.
Abstract.
T. Eliassi-Rad & J. Shavlik (2001).
A Theory-Refinement Approach to Information Extraction. Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA.
Abstract.
M. Craven, D. Page, J. Shavlik, J. Bockhorst & J. Glasner (2000).
Probabilistic Learning Approach to Whole-Genome Operon Prediction. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, La Jolla, CA.
Abstract.
M. Craven, D. Page, J. Shavlik, J. Bockhorst & J. Glasner (2000).
Using Multiple Levels of Learning and Diverse Evidence Sources to Uncover Coordinately Controlled Genes. Proceedings of the Seventeenth International Conference on Machine Learning, Palo Alto, CA.
Abstract.
J. Goecks & J. Shavlik (2000).
Learning Users' Interests by Unobtrusively Observing Their Normal Behavior. Proceedings of the 2000 International Conference on Intelligent User Interfaces, pp. 129-132, New Orleans, LA.
Abstract.
J. Shavlik, S. Calcari, T. Eliassi-Rad & J. Solock (1999).
An Instructable, Adaptive Interface for Discovering and Monitoring Information on the World-Wide Web. Proceedings of the 1999 International Conference on Intelligent User Interfaces, pp. 157 - 160, Redondo Beach, CA.
Abstract.
C. Allex, S. Baldwin, J. Shavlik & F. Blattner (1997).
Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations. Proceedings, Fifth International Conference on Intelligent Systems for Molecular Biology, pp. , Halkidiki, Greece. AAAI Press.
Abstract.
C. Allex, S. Baldwin, J. Shavlik & F. Blattner (1996).
Improving the Quality of Automatic DNA Sequence Assembly using Fluorescent Trace-Data Classifications. Proceedings, Fourth International Conference on Intelligent Systems for Molecular Biology, pp. 3-14, St. Louis, MO. AAAI Press.
Abstract.
K. Cherkauer & J. Shavlik (1996).
Growing Simpler Decision Trees to Facilitate Knowledge Discovery. Proceedings, Second International Conference on Knowledge Discovery and Data Mining, pp. 315-318, Portland, OR. AAAI Press.
Abstract.
D. Opitz & J. Shavlik (1996).
Generating Accurate and Diverse Members of a Neural-Network Ensemble. Advances in Neural Information Processing Systems, pp. 535-543, Denver, CO. MIT Press.
Data. Abstract.
K. Cherkauer & J. Shavlik (1996).
Rapid Quality Estimation of Neural Network Input Representations. Advances in Neural Information Processing Systems, pp. 45-51, Denver, CO. MIT Press.
Abstract.
M. Craven & J. Shavlik (1996).
Extracting Tree-Structured Representations of Trained Networks. Advances in Neural Information Processing Systems, pp. 24-30, Denver, CO. MIT Press.
Code. Abstract.
J. Jackson & M. Craven (1996).
Learning Sparse Perceptrons. Advances in Neural Information Processing Systems, pp. 654-662, Denver, CO. MIT Press.
Abstract.
J. Shavlik (1996).
An Overview of Research at Wisconsin on Knowledge-Based Neural Networks. Proceedings of the International Conference on Neural Networks, pp. 65-69, Washington, DC.
Abstract.
R. Maclin & J. Shavlik (1995).
Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 524-530, Montreal, Canada.
Abstract.
M. Craven, R. Mural, L. Hauser & E. Uberbacher (1995).
Predicting Protein Folding Classes without Overly Relying on Homology. Proceedings of the Third International Conference on Intelligent Systems for Molecular Biology, pp. 98-106, Cambridge, England. AAAI Press.
D. Opitz & J. Shavlik (1994).
Using Genetic Search to Refine Knowledge-Based Neural Networks. Machine Learning: Proceedings of the Eleventh International Conference, pp. 208-216, New Brunswick, NJ. Morgan Kaufmann.
Data. Abstract.
M. Craven & J. Shavlik (1994).
Using Sampling and Queries to Extract Rules from Trained Neural Networks. Proceedings of the Eleventh International Conference on Machine Learning, pp. 37-45, New Brunswick, NJ.
Abstract.
R. Maclin & J. Shavlik (1994).
Incorporating Advice into Agents that Learn from Reinforcements. Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 694-699, Seattle, WA.
Abstract.
D. Opitz & J. Shavlik (1993).
Heuristically Expanding Knowledge-Based Neural Networks. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1360-1365, Chambery, France.
Data. Abstract.
M. Craven & J. Shavlik (1993).
Learning to Represent Codons: A Challenge Problem for Constructive Induction. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1319-1324, Chambery, France.
Data. Abstract.
K. Cherkauer & J. Shavlik (1993).
Protein Structure Prediction: Selecting Salient Features From Large Candidate Pools. Proceedings of the First International Conference on Intelligent Systems for Molecular Biology, pp. 74-82, Bethesda, MD. AAAI Press.
Abstract.
M. Craven & J. Shavlik (1993).
Learning Symbolic Rules Using Artificial Neural Networks. Proceedings of the Tenth International Conference on Machine Learning, pp. 73-80, Amherst, MA. Morgan Kaufmann.
Abstract.
M. Craven & J. Shavlik (1993).
Learning to Predict Reading Frames in E . coli DNA Sequences. Proceedings of the 26th Hawaii International Conference on System Sciences, pp. 773-782, Wailea, HI. IEEE Computer Society Press.
Data. Abstract.
G. Towell & J. Shavlik (1992).
Using Symbolic Learning to Improve Knowledge-Based Neural Networks. Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 177-182, San Jose, CA.
Abstract.
R. Maclin & J. Shavlik (1992).
Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 165-170, San Jose, CA.
Abstract.
G. Scott, J. Shavlik & W. Ray (1992).
Refining PID Controllers using Neural Networks. Advances in Neural Information Processing Systems, pp. 555-562, Denver, CO. Morgan Kaufmann.
Abstract.
G. Towell & J. Shavlik (1992).
Interpretation of Artificial Neural Networks: Mapping knowledge-based Neural Networks into Rules. Advances in Neural Information Processing Systems, pp. 977-984, Denver, CO. Morgan Kaufmann.
Data. Abstract.
M. Noordewier, G. Towell & J. Shavlik (1991).
Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences. Advances in Neural Information Processing Systems, pp. 530-536, Denver, CO. Morgan Kaufmann.
Data. Abstract.
G. Towell, J. Shavlik & M. Noordewier (1990).
Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks. Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 861-866, Boston, MA.
Data. Abstract.

Book Chapters

L. Torrey, J. Shavlik, T. Walker & R. Maclin (2009).
Transfer Learning via Advice Taking. In J. Koronacki, S. Wirzchon, Z. Ras & J. Kacprzyk, editors, Recent Advances in Machine Learning, dedicated to the memory of Ryszard S. Michalski. Springer Studies in Computational Intelligence.
Abstract.
L. Torrey & J. Shavlik (2009).
Transfer Learning. In E. Soria, J. Martin, R. Magdalena, M. Martinez & A. Serrano, editors, Handbook of Research on Machine Learning Applications. IGI Global.
Abstract.
F. DiMaio, A. Soni & J. Shavlik (2008).
Machine Learning in Structural Biology: Interpreting 3D Protein Images. In S. Mitra, S. Datta, T. Perkins & G. Michailidis, editors, Introduction to Machine Learning and Bioinformatics, pp. 237-276. Chapman & Hall/CRC Press.
(The on-line version is a pre-publication version of the chapter)
Abstract.
L. Torrey, J. Shavlik, T. Walker & R. Maclin (2008).
Rule Extraction for Transfer Learning. In J. Diederich, editor, Rule Extraction from Support Vector Machines, pp. 67-82. Springer.
Abstract.
J. Davis, E. Burnside, I. Dutra, D. Page, R. Ramakrishnan, J. Shavlik & V. Santos Costa (2007).
Learning a New View of a Database: With an Application in Mammography. In L. Getoor & B. Taskar, editors, Introduction to Statistical Relational Learning. MIT Press.
Abstract.
R. Mooney, P. Melville, L. Tang, J. Shavlik, I. Dutra, D. Page & V. Santos Costa (2003).
Relational Data Mining with Inductive Logic Programming for Link Discovery. In H. Kargupta & A. Joshi, editors, Data Mining: Next Generation Challenges and Future Directions, pp. 239-254. AAAI/MIT Press.
Abstract.
T. Eliassi-Rad & J. Shavlik (2003).
Intelligent Web Agents that Learn to Retrieve and Extract Information. In P.S. Szczepaniak, F. Segovia, J. Kacprzyk & L.A. Zadeh, editors, Intelligent Exploration of the Web, pp. 255-274. Springer-Verlag.
Abstract.
M. Craven & J. Shavlik (1995).
Investigating the Value of a Good Input Representation. In T. Petsche, S. Hanson & J. Shavlik, editors, Computational Learning Theory and Natural Learning Systems, Volume III. MIT Press.
D. Opitz & J. Shavlik (1995).
Using Heuristic Search to Expand Knowledge-Based Neural Networks. In T. Petsche, S. Hanson & J. Shavlik, editors, Computational Learning Theory and Natural Learning Systems, Volume III, pp. 3-19. MIT Press.
R. Maclin & J. Shavlik (1994).
Refining Algorithms with Knowledge-Based Neural Networks: Improving the Chou-Fasman Algorithm for Protein Folding. In S. Hanson, G. Drastal & R. Rivest, editors, Computational Learning Theory and Natural Learning Systems, Volume I. MIT Press.
K. Cherkauer & J. Shavlik (1994).
Selecting Salient Features for Machine Learning from Large Candidate Pools through Parallel Decision-Tree Construction. In H. Kitano, editor, Massively Parallel Artificial Intelligence, pp. 102-136. AAAI Press/The MIT Press, Menlo Park, CA.
Abstract.
G. Towell & J. Shavlik (1993).
Refining Symbolic Knowledge Using Neural Networks. In R. S. Michalski & G. Tecuci, editors, Machine Learning: An Integrated Approach. Morgan Kaufmann, San Mateo, CA.

Workshop Papers

X. Li, C. Guo, W. Chu, Y. Wang & J. Shavlik (2014).
Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data. Workshop on Personalization: Methods and Applications, at Neural Information Processing Systems (NIPS).
Poster (PDF). Abstract.
X. Li, W. Gao & J. Shavlik (2014).
Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM. SIGIR 2014 Workshop on Semantic Matching in Information Retrieval.
Abstract.
T. Khot, S. Natarajan & J. Shavlik (2014).
Classification from one class of examples for relational domains. Statistical Relational AI (StarAI) Workshop at AAAI.
Abstract.
T. Khot, C. Zhang, S. Natarajan, C. Re, J. Shavlik (2013).
Bootstrapping Knowledge Base Acceleration. The Twenty-Second Text REtrieval Conference Proceedings (TREC 2013).
Abstract.
A. Narayan-Chen, L. Xu & J. Shavlik (2013).
An Empirical Evaluation of Machine Learning Approaches for Angry Birds. IJCAI Symposium on AI in Angry Birds.
Poster (PPTX). Slides (PPTX). Abstract.
T. Khot, S. Srivastava, S. Natarajan & J. Shavlik (2012).
Learning Relational Structure for Temporal Relation Extraction. Statistical Relational Learning AI (StarAI) Workshop at UAI.
Poster (PDF). Abstract.
F. Niu, C. Zhang, C. Re & J. Shavlik (2012).
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference. Workshop on Very Large Data Search.
Abstract.
T. Khot, S. Natarajan, K. Kersting & J. Shavlik (2012).
Structure Learning with Hidden Data in Relational Domains. Statistical Relational Learning Workshop at ICML.
Abstract.
G. Kunapuli & J. Shavlik (2011).
Mirror Descent for Metric Learning. Beyond Mahalanobis: Supervised Large Scale Learning of Similarity Workshop at NIPS.
Poster (PDF). Abstract.
G. Kunapuli, R. Maclin & J. Shavlik (2011).
Advice Refinement for Knowledge-Based Support Vector Machines. ICML 2011 Workshop on Combining Learning Strategies for Reducing Label Cost.
Poster (PDF). Abstract.
S. Natarajan, T. Khot, D. Lowd, P. Tadepalli, K. Kersting & J. Shavlik (2010).
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. Workshop on Statistical Relational AI at AAAI, Atlanta, GA.
Abstract.
S. Natarajan, G. Kunauli, R. Maclin, D. Page, C. O'Reilly, T. Walker & J. Shavlik (2010).
Learning from Human Teachers: Issues and Challenges in Bootstrap Learning. AAMAS 2010 Workshop on Agents Learning Interactively from Human Teachers, Toronto, Canada.
Slides (PPTX). Abstract.
H. Nassif, R. Woods, E. Burnside, M. Ayvaci, J. Shavlik & D. Page (2009).
Information Extraction for Clinical Data Mining: A Mammography Case Study. Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, Miami, FL.
Abstract.
S. Natarajan, G. Kunapuli, C. O'Reilly, R.Maclin, T. Walker, D. Page & J.Shavlik (2009).
ILP for Bootstrapped Learning: A Layered Approach to Automating the ILP Setup Problem. Presented at the Nineteenth Conference on Inductive Logic Programming, Leuven, Belgium.
Abstract.
R. De Salvo Braz, S. Natarajan, H. Bui, J. Shavlik & S. Russell (2009).
Anytime Lifted Belief Propagation. 6th International Workshop on Statistical Relational Learning, Leuven, Belgium.
Abstract.
L. Torrey, T. Walker, R. Maclin & J. Shavlik (2008).
Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning. AAAI Fall Symposium on Naturally Inspired AI, Washington, DC.
Slides (PPT). Abstract.
L. Torrey, J. Shavlik, S. Natarajan, P. Kuppili & T. Walker (2008).
Transfer in Reinforcement Learning via Markov Logic Networks. AAAI'08 Workshop on Transfer Learning for Complex Tasks, Chicago, IL.
Slides (PPT). Abstract.
L. Torrey, J. Shavlik, T. Walker & R. Maclin (2006).
Relational Skill Transfer via Advice Taking. ICML'06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburg, PA.
Abstract.
M. Goadrich, L. Oliphant & J. Shavlik (2005).
Learning to Extract Genic Interactions Using Gleaner. ICML'05 Workshop on Learning Language in Logic (LLL05), Bonn, Germany.
Abstract.
R. Maclin, J. Shavlik, L. Torrey & T. Walker (2005).
Knowledge Based Support Vector Regression for Reinforcement Learning. IJCAI'05 Workshop on Reasoning, Representation, and Learning in Computer Games, Edinburgh, Scotland.
Poster (PDF). Abstract.
T. Walker, J. Shavlik & R. Maclin (2004).
Relational Reinforcement Learning via Sampling the Space of First-Order Conjunctive Features. Proceedings of the ICML Workshop on Relational Reinforcement Learning, Banff, Canada.
Abstract.
G. Kuhlmann, P. Stone, R. Mooney & J. Shavlik (2004).
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer. Proceedings of the AAAI Workshop on Supervisory Control of Learning and Adaptive Systems, San Jose, CA, USA.
Abstract.
F. DiMaio & J. Shavlik (2003).
Speeding Up Relational Data Mining by Learning to Estimate Candidate Hypothesis Scores. Proceedings of the ICDM Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, USA.
A longer and updated version of this paper appears in Proceedings of the Fourteenth International Conference on Inductive Logic Programming (2004)
Slides (PPT). Abstract.
F. DiMaio, J. Shavlik & G. Phillips (2003).
Using Pictorial Structures to Identify Proteins in X-ray Crystallographic Electron Density Maps. Working Notes of the ICML Workshop on Machine Learning in Bioinformatics, Washington DC, USA.
Slides (PPT). Abstract.
R. Mooney, P. Melville, L. Tang, J. Shavlik, I. Dutra, D. Page & V. Santos Costa (2002).
Relational Data Mining with Inductive Logic Programming for Link Discovery. Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, Maryland, USA.
A longer and updated version of this paper appears as a chapter in ``Data Mining: Next Generation Challenges and Future Directions'', H. Kargupta and A. Joshi (eds.), by AAAI/MIT Press
Abstract.
J. Goecks & J. Shavlik (1999).
Automatically Labeling Web Pages Based on Normal User Actions. Proceedings of the IJCAI-99 Workshop on Machine Learning for Information Filtering, Stockholm, Sweden.
Abstract.
J. Shavlik & T. Eliassi-Rad (1998).
Intelligent Agents for Web-Based Tasks: An Advice-Taking Approach. Working Notes of the AAAI/ICML-98 Workshop on Learning for Text Categorization, Madison, WI.
Abstract.
J. Shavlik & T. Eliassi-Rad (1998).
Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach. Presented at the Conf on Automated Learning and Discovery Workshop on Learning from Text and the Web, Pittsburgh, PA.
Abstract.
K. Cherkauer (1996).
Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks. Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Wkshp, 13th Nat Conf on Artificial Intelligence, pp. 15-21, Portland, OR.
Abstract.
M. Craven & J. Shavlik (1995).
Extracting Comprehensible Concept Representations from Trained Neural Networks. Presented at the IJCAI Workshop on Comprehensibility in Machine Learning, Montreal, Quebec, Canada.
Abstract.
K. Cherkauer & J. Shavlik (1995).
Rapidly Estimating the Quality of Input Representations for Neural Networks. Working Notes of the IJCAI-95 Workshop on Data Engineering for Inductive Learning, Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada.
Abstract.
D. Opitz & J. Shavlik (1994).
Genetically Refining Topologies of Knowledge-Based Neural Networks. International Symposium on Integrating Knowledge and Neural Heuristics, pp. 57-66, Pensacola, FL.
Abstract.
R. Maclin & J. Shavlik (1991).
Refining Domain Theories Expressed as Finite-State Automata. Proceedings of the Eighth International Machine Learning Workshop, pp. 524-528, Evanston, IL.
Abstract.
G. Towell, M. Craven & J. Shavlik (1991).
Constructive Induction in Knowledge-Based Neural Networks. Proceedings of the Eighth International Machine Learning Workshop, pp. 213-217, Evanston, IL.
Data. Abstract.
J. Shavlik (1991).
Finding Genes by Case-Based Reasoning in the Presence of Noisy Case Boundaries. Proceedings of the DARPA Cased-Based Reasoning Workshop, pp. 327-338.
Abstract.

Tech Reports and Working Papers (not published elsewhere)

F. DiMaio & J. Shavlik (2006).
Improving the Efficiency of Belief Propagation in Large Highly Connected Graphs. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 06-1.
Abstract.
L. Torrey, J. Shavlik, T. Walker & R. Maclin (2006).
Transfer Learning in Robocup. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 06-2.
Abstract.
M. Molla, P. Andreae & J. Shavlik (2004).
Building Genome Expression Models using Microarray Expression Data and Text. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 04-1.
Abstract.
G. Fung, O. Mangasarian & J. Shavlik (2001).
Knowledge-based Support Vector Machine Classifiers. Data Mining Institute, University of Wisconsin, DMI TR 01-09.
(A shorter version of this paper appears in Advances in Neural Information Processing [NIPS], 2002)
Abstract.
M. Craven & J. Shavlik (1999).
Rule Extraction: Where Do We Go from Here?. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 99-1.
Abstract.
D. Opitz & J. Shavlik (1995).
Generating Accurate and Diverse Members of a Neural-Network Ensemble. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 95-2.
(A version of this paper appears in Advances in Neural Information Processing, vol. 8, 1996)
Abstract.
R. Maclin & J. Shavlik (1994).
Incorporating Advice into Agents that Learn from Reinforcements. Department of Computer Sciences, University of Wisconsin, UW TR 1227.
(A shorter version appears in AAAI-94.)
Abstract.
E. Gutstein (1992).
Learning from Students to Improve an Intelligent Tutoring System. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 92-3.

M.S. Theses

Benjamin Geisler (2002).
An Empirical Study of Machine Learning Algorithms Applied to Modeling Player Behavior in a 'First Person Shooter' Video Game. M.S. thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Abstract.

This publication is available in PDF and available in postscript.

PhD Theses

Finn Kuusisto (2015).
Machine Learning for Medical Decision Support and Individualized Treatment Assignment. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Slides (PPTX). Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Ce Zhang (2015).
DeepDive: A Data Management System for Automatic Knowledge Base Construction. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Slides (PPTX). Prelim. Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Tushar Khot (2014).
Efficient Learning of Statistical Relational Models. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Slides (PPTX). Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Trevor Walker (2011).
Broadening the Applicability of Relational Learning. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-11-1698)
Slides (PPTX). Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Ameet Soni (2011).
Techniques for Improved Probabilistic Inference In Protein-Structure Determination via X-Ray Crystallography. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-11-1703)
Slides (PPTX). Code. Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Louis Oliphant (2009).
Adaptively Finding and Combining First-Order Rules for Large, Skewed Data Sets. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-09-1661)
Abstract.
Lisa Torrey (2009).
Relational Transfer in Reinforcement Learning. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-09-1657)
Slides (PPTX). Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Frank DiMaio (2007).
Probabilistic Methods for Interpreting Electron-Density Maps. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-07-1617)
Slides (PPT). Code. Data. Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Michael Molla (2007).
Novel Uses for Machine Learning and Other Computational Methods for the Design and Interpretation of Genetic Microarrays. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-07-1612)
Slides (PPT). Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Mark Goadrich (2007).
Learning Ensembles of First-Order Clauses that Optimize Precision-Recall Curves. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-07-1609)
Slides (PPT). Data. Abstract.

This publication is available in PDF.

The slides for this publication are available in Microsoft PowerPoint.
Tina Eliassi-Rad (2001).
Building Intelligent Agents that Learn to Retrieve and Extract Information. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-01-1431)
Abstract.

This publication is available in PDF and available in postscript.
Carolyn F. Allex (1999).
Computational Methods for Fast and Accurate DNA Fragment Assembly. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-99-1406)
Abstract.

This publication is available in PDF and available in postscript.
Mark W. Craven (1996).
Extracting Comprehensible Models from Trained Neural Networks. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-96-1326)
Code. Abstract.

This publication is available in PDF and available in postscript.
David W. Opitz (1995).
An Anytime Approach to Connectionist Theory Refinement: Refining the Topologies of Knowledge-Based Neural Networks. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-95-1281)(
Data. Abstract.

This publication is available in PDF and available in postscript.
Richard Maclin (1995).
Learning from Instruction and Experience: Methods for Incorporating Procedural Domain Theories into Knowledge-Based Neural Networks. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report CS-TR-95-1285)
Abstract.

This publication is contained in the following 2 postscript files
File 1, File 2
and contained in the following 2 PDF files
File 1, File 2.
Eric Gutstein (1993).
SIFT: A Self-Improving Fractions Tutor. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
Geoffrey G. Towell (1991).
Symbolic Knowledge and Neural Networks: Insertion, Refinement and Extraction. PhD thesis, Department of Computer Sciences, University of Wisconsin-Madison.
(Also appears as UW Technical Report 1072)
Data. Abstract.

This publication is contained in the following 4 postscript files
File 1, File 2, File 3, File 4
and contained in the following 4 PDF files
File 1, File 2, File 3, File 4.

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