Selected Publications by Mark Craven

• B. Settles & M. Craven (2008).
  An Analysis of Active Learning Strategies for Sequence Labeling Tasks.
  To appear in Proceedings of the Conference on Empirical Methods in Natural Language Processing, ACL Press.

• A. Smith, A. Vollrath, C. Bradfield & M. Craven (2008).
  Similarity Queries for Temporal Toxicogenomic Expression Profiles.
  PLoS Computational Biology 4(7).

• A. Smith & M. Craven (2008).
  Fast Multisegment Alignments for Temporal Expression Profiles.
  Proceedings of the 7th International Conference on Computational Systems Bioinformatics. World Scientific Press.

• K. Noto & M. Craven (2008).
  Learning Hidden Markov Models for Regression using Path Aggregation.
  Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence .

• B. Settles, S. Ray & M. Craven (2008).
  Multiple-Instance Active Learning.
  To appear in Advances in Neural Information Processing Systems (NIPS-20), MIT Press.

• Y. Pan, T. Durfee, J. Bockhorst & M. Craven (2007).
  Connecting Quantitative Regulatory-Network Models to the Genome.
  Bioinformatics 23(13):i367-i376. (special issue: Proceedings of the 15th ISMB and 6th ECCB Conferences)

• K. Noto & M. Craven (2007).
  Learning Probabilistic Models of cis-Regulatory Modules that Represent Logical and Spatial Aspects.
  Bioinformatics 23(2):e156-e162. (special issue: Proceedings of the 5th European Conference on Computational Biology)

• A. Goldberg, D. Andrzejewski, J. Van Gael, B. Settles, X. Zhu & M. Craven (2007).
  Ranking Biomedical Passages for Relevance and Diversity.
  Proceedings of the Fifteenth Text Retrieval Conference (TREC 2006).

• K. Noto & M. Craven (2006).
  A Specialized Learner for Inferring Structured cis-Regulatory Modules.
  BMC Bioinformatics, 7:528.

• T. Brow, B. Settles & M. Craven (2006).
  Classifying Biomedical Articles by Making Localized Decisions.
  Proceedings of the Fourteenth Text Retrieval Conference (TREC 2005).

• S. Ray & M. Craven (2005).
  Supervised versus Multiple Instance Learning: An Empirical Comparison.
  Proceedings of the 22nd International Conference on Machine Learning, 697-704. ACM Press.

• J. Bockhorst & M. Craven (2005).
  Markov Networks for Detecting Overlapping Elements in Sequence Data.
  Advances in Neural Information Processing Systems (NIPS-17), 193-200. MIT Press.

• S. Ray & M. Craven (2005).
  Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text.
  BMC Bioinformatics, 6(Suppl. 1):S18

• B. Settles & M. Craven (2005).
  Exploiting Zone Information, Syntactic Features, and Informative Terms in Gene Ontology Annotation from Biomedical Documents.
  Proceedings of the Thirteenth Text Retrieval Conference (TREC 2004).

• K. Hayes, A. Vollrath, G. Zastrow, B. McMillan, M. Craven, S. Jovanovich, J. Walisser, D. Rank, S. Penn, J. Reddy, R. Thomas & C. Bradfield (2005).
  EDGE: A Centralized Resource for the Comparison, Analysis and Distribution of Toxicogenomic Information.
  Molecular Pharmacology, 67(4):1360-1368.

• K. Noto & M. Craven (2004).
  Learning Regulatory Network Models that Represent Regulator States and Roles.
  In E. Eskin and C. Workman (Editors) Regulatory Genomics: RECOMB 2004 International Workshop, 52-64. Springer-Verlag.

• G. Yao, M. Craven, N. Drinkwater & C. Bradfield (2004).
  Interaction Networks in Yeast Define and Enumerate the Signaling Steps of the Vertebrate Aryl Hydrocarbon Receptor.
  PLoS Biology, 2(3):356-367.

• M. Skounakis, M. Craven & S. Ray (2003).
  Hierarchical Hidden Markov Models for Information Extraction.
  Proceedings of the 18th International Joint Conference on Artificial Intelligence, 427-433. Morgan Kaufmann.

• M. Skounakis & M. Craven (2003).
  Evidence Combination in Biomedical Natural-Language Processing.
  Proceedings of the 3rd Workshop on Data Mining in Bioinformatics, held in conjunction with KDD 2003.

• J. Bockhorst, Y. Qiu, J. Glasner, M. Liu, F. Blattner & M. Craven (2003).
  Predicting Bacterial Transcription Units using Sequence and Expression Data.
  Bioinformatics, 19(Supplement):34-43.
  (special issue: Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology)

• J. Bockhorst, M. Craven, D. Page, J. Shavlik & J. Glasner (2003).
  A Bayesian Network Approach to Operon Prediction.
  Bioinformatics, 19(10):1227-1235.

• D. Page & M. Craven (2003).
  Biological Applications of Multi-Relational Data Mining.
  SIGKDD Explorations, 5(1):69-79.

• M. Craven (2003).
  The Genomics of a Signaling Pathway: A KDD Cup Challenge Task.
  SIGKDD Explorations, 4(2):97-98.

• J. Bockhorst & M. Craven (2002).
  Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data.
  Proceedings of the 19th International Conference on Machine Learning, 43-50. Morgan Kaufmann.

• R. Thomas, D. Rank, S. Penn, G. Zastrow, K. Hayes, K. Pande, E. Glover, T. Silander, M. Craven, J. Reddy, S. Jovanovich & C. Bradfield (2001).
  Identification of Toxicologically Predictive Gene Sets Using cDNA Microarrays.
  Molecular Pharmacology, 60:1189-1194.

• J. Bockhorst & M. Craven (2001).
  Refining the Structure of a Stochastic Context-Free Grammar.
  Proceedings of the 17th International Joint Conference on Artificial Intelligence, 1315-1320. Morgan Kaufmann.

• S. Ray & M. Craven (2001).
  Representing Sentence Structure in Hidden Markov Models for Information Extraction.
  Proceedings of the 17th International Joint Conference on Artificial Intelligence, 1273-1279. Morgan Kaufmann.

• M. Craven & S. Slattery (2001).
  Relational Learning with Statistical Predicate Invention: Better Models for Hypertext.
  Machine Learning, 43(1-2): 97-119.

• M. Craven, D. Page, J. Shavlik, J. Bockhorst & J. Glasner (2000).
  A Probabilistic Learning Approach to Whole-Genome Operon Prediction.
  Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, 116-127. AAAI Press.

• 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 17th International Conference on Machine Learning, 199-206. Morgan Kaufmann.

• M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam & S. Slattery (2000).
  Learning to Construct Knowledge Bases from the World Wide Web.
  Artificial Intelligence, 118(1-2): 69-113.

• M. Craven & J. Kumlien (1999).
  Constructing Biological Knowledge Bases by Extracting Information from Text Sources.
  Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology, 77-86, AAAI Press.

• M. Craven & J. Shavlik (1999).
  Rule Extraction: Where Do We Go from Here?
  University of Wisconsin Machine Learning Research Group Working Paper 99-1.

• S. Slattery & M. Craven (1998).
  Combining Statistical and Relational Methods for Learning in Hypertext Domains.
  Proceedings of the 8th International Conference on Inductive Logic Programming, pp. 38-52. Springer Verlag.

• M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam & S. Slattery (1998).
  Learning to Extract Symbolic Knowledge from the World Wide Web.
  Proceedings of the 15th National Conference on Artificial Intelligence, pp. 509-516. AAAI Press.

• M. Craven, S. Slattery & K. Nigam (1998).
  First-Order Learning for Web Mining.
  Proceedings of the 10th European Machine Learning Conference, 250-255. Springer Verlag.

• M. Craven & J. Shavlik (1997).
  Understanding Time-Series Networks: A Case Study in Rule Extraction.
  International Journal of Neural Systems 8(4): 373-384.

• M. Craven & J. Shavlik (1997).
  Using Neural Networks for Data Mining.
  Future Generation Computer Systems (Special Issue on Data Mining) 13:211-229.

• M. 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)

• M. Craven & J. Shavlik (1995).
  Extracting Tree-Structured Representations of Trained Networks.
  Advances in Neural Information Processing Systems (NIPS-8), pp. 24-30. MIT Press.

• J. Jackson & M. Craven (1995).
  Learning Sparse Perceptrons.
  Advances in Neural Information Processing Systems (NIPS-8), pp. 654-660. MIT Press.

• M. Craven, R. Mural, L. Hauser & E. Uberbacher (1995).
  Predicting Protein Folding Classes without Overly Relying on Homology.
  Proceedings of the 3rd International Conference on Intelligent Systems for Molecular Biology, pp.98-106. AAAI Press.

• M. Craven & J. Shavlik (1994).
  Using Sampling and Queries to Extract Rules from Trained Neural Networks.
  Proceedings of the 11th International Conference on Machine Learning, pp. 37-45. Morgan Kaufmann.

• M. Craven & J. Shavlik (1993).
  Learning to Represent Codons: A Challenge Problem for Constructive Induction.
  Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1319-1324. Morgan Kaufmann.

• M. Craven & J. Shavlik (1993).
  Learning Symbolic Rules Using Artificial Neural Networks.
  Proceedings of the 10th International Conference on Machine Learning, pp. 73-80. Morgan Kaufmann.

Last modified: Fri July 18 10:34:54 CDT 2008