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2020 – today
- 2022
[c32]Akshay Sood, Mark Craven:
Feature Importance Explanations for Temporal Black-Box Models. AAAI 2022: 8351-8360- 2021
- [i3]Akshay Sood, Mark Craven:
Feature Importance Explanations for Temporal Black-Box Models. CoRR abs/2102.11934 (2021) - 2020
- [j17]Yuriy Sverchkov, Yi-Hsuan Ho, Audrey P. Gasch, Mark Craven:
Context-Specific Nested Effects Models. J. Comput. Biol. 27(3): 403-417 (2020)
2010 – 2019
- 2019
- [j16]Sunyoung Shin, Rebecca Hudson, Christopher Harrison
, Mark Craven, Sündüz Keles:
atSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding. Bioinform. 35(15): 2657-2659 (2019) - [j15]Sid Kiblawi, Deborah Chasman, Amanda Henning, Eunju Park, Hoifung Poon, Michael Gould, Paul Ahlquist, Mark Craven:
Augmenting subnetwork inference with information extracted from the scientific literature. PLoS Comput. Biol. 15(6) (2019) - [c31]Kyubin Lee, Akshay Sood, Mark Craven:
Understanding Learned Models by Identifying Important Features at the Right Resolution. AAAI 2019: 4155-4163 - 2018
- [c30]Yuriy Sverchkov, Yi-Hsuan Ho, Audrey P. Gasch, Mark Craven:
Context-Specific Nested Effects Models. RECOMB 2018: 194-210 - [i2]Kyubin Lee, Akshay Sood, Mark Craven:
Understanding Learned Models by Identifying Important Features at the Right Resolution. CoRR abs/1811.07279 (2018) - 2017
- [j14]Yuriy Sverchkov, Mark Craven:
A review of active learning approaches to experimental design for uncovering biological networks. PLoS Comput. Biol. 13(6) (2017) - 2014
- [j13]Deborah Chasman, Brandi Gancarz, Linhui Hao, Michael Ferris, Paul Ahlquist, Mark Craven:
Inferring Host Gene Subnetworks Involved in Viral Replication. PLoS Comput. Biol. 10(5) (2014) - 2013
- [j12]Linhui Hao, Qiuling He, Zhishi Wang, Mark Craven, Michael A. Newton, Paul Ahlquist:
Limited Agreement of Independent RNAi Screens for Virus-Required Host Genes Owes More to False-Negative than False-Positive Factors. PLoS Comput. Biol. 9(9) (2013) - 2012
- [j11]Andreas Vlachos, Mark Craven:
Biomedical event extraction from abstracts and full papers using search-based structured prediction. BMC Bioinform. 13(S-11): S5 (2012) - [i1]Keith Noto, Mark Craven:
Learning Hidden Markov Models for Regression using Path Aggregation. CoRR abs/1206.3275 (2012) - 2011
- [c29]Andreas Vlachos, Mark Craven:
Biomedical Event Extraction from Abstracts and Full Papers using Search-based Structured Prediction. BioNLP@ACL (Shared Task) 2011: 36-40 - [c28]Andreas Vlachos, Mark Craven:
Search-based Structured Prediction applied to Biomedical Event Extraction. CoNLL 2011: 49-57 - [c27]David Andrzejewski, Xiaojin Zhu, Mark Craven, Benjamin Recht:
A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic. IJCAI 2011: 1171-1177 - 2010
- [c26]Andreas Vlachos, Mark Craven:
Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression. CoNLL Shared Task 2010: 18-25
2000 – 2009
- 2009
- [j10]Adam A. Smith, Aaron L. Vollrath, Christopher A. Bradfield, Mark Craven:
Clustered alignments of gene-expression time series data. Bioinform. 25(12) (2009) - [j9]Aaron L. Vollrath, Adam A. Smith, Mark Craven, Christopher A. Bradfield:
EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments. BMC Bioinform. 10: 280 (2009) - [c25]David Andrzejewski, Xiaojin Zhu, Mark Craven:
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. ICML 2009: 25-32 - 2008
- [j8]Adam A. Smith, Aaron L. Vollrath, Christopher A. Bradfield, Mark Craven:
Similarity Queries for Temporal Toxicogenomic Expression Profiles. PLoS Comput. Biol. 4(7) (2008) - [c24]Burr Settles, Mark Craven:
An Analysis of Active Learning Strategies for Sequence Labeling Tasks. EMNLP 2008: 1070-1079 - [c23]Mark Craven:
Learning Expressive Models of Gene Regulation. ILP 2008: 4 - [c22]Keith Noto, Mark Craven:
Learning Hidden Markov Models for Regression using Path Aggregation. UAI 2008: 444-451 - [e2]Ana L. C. Bazzan, Mark Craven, Natália Florencio Martins:
Advances in Bioinformatics and Computational Biology, Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings. Lecture Notes in Computer Science 5167, Springer 2008, ISBN 978-3-540-85556-9 [contents] - 2007
- [j7]Keith Noto, Mark Craven:
Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects. Bioinform. 23(2): 156-162 (2007) - [c21]Yue Pan, Tim Durfee, Joseph Bockhorst, Mark Craven:
Connecting quantitative regulatory-network models to the genome. ISMB/ECCB (Supplement of Bioinformatics) 2007: 367-376 - [c20]Burr Settles, Mark Craven, Soumya Ray:
Multiple-Instance Active Learning. NIPS 2007: 1289-1296 - 2006
- [j6]Keith Noto, Mark Craven:
A specialized learner for inferring structured cis-regulatory modules. BMC Bioinform. 7: 528 (2006) - [c19]Andrew B. Goldberg, David Andrzejewski, Jurgen Van Gael, Burr Settles, Xiaojin Zhu, Mark Craven:
Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. TREC 2006 - [e1]Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos:
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006. ACM 2006, ISBN 1-59593-339-5 [contents] - 2005
- [j5]Soumya Ray, Mark Craven:
Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text. BMC Bioinform. 6(S-1) (2005) - [c18]Soumya Ray, Mark Craven:
Supervised versus multiple instance learning: an empirical comparison. ICML 2005: 697-704 - [c17]Thomas Brow, Burr Settles, Mark Craven:
Classifying Biomedical Articles by Making Localized Decisions. TREC 2005 - 2004
- [c16]Aaron E. Darling, Bob Mau, Mark Craven, Nicole T. Perna:
Multiple Alignment of Rearranged Genomes. CSB 2004: 738-739 - [c15]Joseph Bockhorst, Mark Craven:
Markov Networks for Detecting Overalpping Elements in Sequence Data. NIPS 2004: 193-200 - [c14]Keith Noto, Mark Craven:
Learning Regulatory Network Models that Represent Regulator States and Roles. Regulatory Genomics 2004: 52-64 - [c13]Burr Settles, Mark Craven:
Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents. TREC 2004 - 2003
- [j4]David Page, Mark Craven:
Biological applications of multi-relational data mining. SIGKDD Explor. 5(1): 69-79 (2003) - [c12]Marios Skounakis, Mark Craven, Soumya Ray:
Hierarchical Hidden Markov Models for Information Extraction. IJCAI 2003: 427-433 - [c11]Joseph Bockhorst, Yu Qiu, Jeremy D. Glasner, Mingzhu Liu, Frederick R. Blattner, Mark Craven:
Predicting bacterial transcription units using sequence and expression data. ISMB (Supplement of Bioinformatics) 2003: 34-43 - [c10]Marios Skounakis, Mark Craven:
Evidence combination in biomedical natural-language processing. BIOKDD 2003: 25-32 - 2002
- [j3]Mark Craven:
The Genomics of a Signaling Pathway: A KDD Cup Challenge Task. SIGKDD Explor. 4(2): 97-98 (2002) - [c9]Joseph Bockhorst, Mark Craven:
Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data. ICML 2002: 43-50 - 2001
- [j2]Mark Craven, Seán Slattery:
Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. Mach. Learn. 43(1/2): 97-119 (2001) - [c8]Soumya Ray, Mark Craven:
Representing Sentence Structure in Hidden Markov Models for Information Extraction. IJCAI 2001: 1273-1279 - [c7]Joseph Bockhorst, Mark Craven:
Refining the Structure of a Stochastic Context-Free Grammar. IJCAI 2001: 1315-1322 - 2000
- [j1]Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery:
Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 118(1-2): 69-113 (2000)
1990 – 1999
- 1999
- [c6]Mark Craven, Johan Kumlien:
Constructing Biological Knowledge Bases by Extracting Information from Text Sources. ISMB 1999: 77-86 - 1998
- [c5]Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery:
Learning to Extract Symbolic Knowledge from the World Wide Web. AAAI/IAAI 1998: 509-516 - [c4]Mark Craven, Seán Slattery, Kamal Nigam:
First-Order Learning for Web Mining. ECML 1998: 250-255 - [c3]Seán Slattery, Mark Craven:
Combining Statistical and Relational Methods for Learning in Hypertext Domains. ILP 1998: 38-52 - 1995
- [c2]Mark Craven, Richard J. Mural, Loren J. Hauser, Edward C. Uberbacher:
Predicting Protein Folding Classes without Overly Relying on Homology. ISMB 1995: 98-106 - [c1]Jeffrey C. Jackson, Mark Craven:
Learning Sparse Perceptrons. NIPS 1995: 654-660
Coauthor Index
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