Andrew McCallum

David M. Blei, J. Andrew Bagnell, Andrew McCallum: Learning with Scope, with Application to Information Extraction and Classification. CoRR abs/1301.0556 (2013)

Sebastian Riedel, Limin Yao, Andrew McCallum: Latent Relation Representations for Universal Schemas. CoRR abs/1301.4293 (2013)

David McClosky, Sebastian Riedel, Mihai Surdeanu, Andrew McCallum, Christopher D. Manning: Combining joint models for biomedical event extraction. BMC Bioinformatics 13(S-11): S9 (2012)

Charles A. Sutton, Andrew McCallum: An Introduction to Conditional Random Fields. Foundations and Trends in Machine Learning 4(4): 267-373 (2012)

Michael L. Wick, Sameer Singh, Andrew McCallum: A Discriminative Hierarchical Model for Fast Coreference at Large Scale. ACL (1) 2012: 379-388

Limin Yao, Sebastian Riedel, Andrew McCallum: Unsupervised Relation Discovery with Sense Disambiguation. ACL (1) 2012: 712-720

Sebastian Riedel, David A. Smith, Andrew McCallum: Parse, Price and Cut--Delayed Column and Row Generation for Graph Based Parsers. EMNLP-CoNLL 2012: 732-743

Sameer Singh, Michael L. Wick, Andrew McCallum: Monte Carlo MCMC: Efficient Inference by Approximate Sampling. EMNLP-CoNLL 2012: 1104-1113

Anton Bakalov, Andrew McCallum, Hanna M. Wallach, David M. Mimno: Topic models for taxonomies. JCDL 2012: 237-240

David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum: MAP Inference in Chains using Column Generation. NIPS 2012: 1853-1861

Pallika H. Kanani, Andrew McCallum: Selecting actions for resource-bounded information extraction using reinforcement learning. WSDM 2012: 253-262

Sebastian Riedel, David A. Smith, Andrew McCallum: Inference by Minimizing Size, Divergence, or their Sum. CoRR abs/1203.3511 (2012)

Kedar Bellare, Gregory Druck, Andrew McCallum: Alternating Projections for Learning with Expectation Constraints. CoRR abs/1205.2660 (2012)

David M. Mimno, Andrew McCallum: Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. CoRR abs/1206.3278 (2012)

Wei Li, David M. Blei, Andrew McCallum: Nonparametric Bayes Pachinko Allocation. CoRR abs/1206.5270 (2012)

Charles A. Sutton, Andrew McCallum: Improved Dynamic Schedules for Belief Propagation. CoRR abs/1206.5291 (2012)

Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira: A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. CoRR abs/1207.1406 (2012)

Charles A. Sutton, Andrew McCallum: Piecewise Training for Undirected Models. CoRR abs/1207.1409 (2012)

Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay: An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching. CoRR abs/1207.4157 (2012)

Andrew McCallum: Efficiently Inducing Features of Conditional Random Fields. CoRR abs/1212.2504 (2012)

Sameer Singh, Amarnag Subramanya, Fernando C. N. Pereira, Andrew McCallum: Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models. ACL 2011: 793-803

Gregory Druck, Andrew McCallum: Toward interactive training and evaluation. CIKM 2011: 947-956

Sebastian Riedel, Andrew McCallum: Fast and Robust Joint Models for Biomedical Event Extraction. EMNLP 2011: 1-12

David M. Mimno, Hanna M. Wallach, Edmund M. Talley, Miriam Leenders, Andrew McCallum: Optimizing Semantic Coherence in Topic Models. EMNLP 2011: 262-272

Limin Yao, Aria Haghighi, Sebastian Riedel, Andrew McCallum: Structured Relation Discovery using Generative Models. EMNLP 2011: 1456-1466

Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum: SampleRank: Training Factor Graphs with Atomic Gradients. ICML 2011: 777-784

Michael L. Wick, Andrew McCallum: Query-Aware MCMC. NIPS 2011: 2564-2572

Gideon S. Mann, Andrew McCallum: Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data. Journal of Machine Learning Research 11: 955-984 (2010)

Michael L. Wick, Andrew McCallum, Gerome Miklau: Scalable Probabilistic Databases with Factor Graphs and MCMC. PVLDB 3(1): 794-804 (2010)

Limin Yao, Sebastian Riedel, Andrew McCallum: Collective Cross-Document Relation Extraction Without Labelled Data. EMNLP 2010: 1013-1023

Gregory Druck, Andrew McCallum: High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models. ICML 2010: 319-326

Sameer Singh, Limin Yao, Sebastian Riedel, Andrew McCallum: Constraint-Driven Rank-Based Learning for Information Extraction. HLT-NAACL 2010: 729-732

Pallika H. Kanani, Andrew McCallum, Shaohan Hu: Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand. PAKDD (1) 2010: 415-427

Sebastian Riedel, Limin Yao, Andrew McCallum: Modeling Relations and Their Mentions without Labeled Text. ECML/PKDD (3) 2010: 148-163

Sebastian Riedel, David A. Smith, Andrew McCallum: Inference by Minimizing Size, Divergence, or their Sum. UAI 2010: 492-499

Michael L. Wick, Andrew McCallum, Gerome Miklau: Scalable Probabilistic Databases with Factor Graphs and MCMC. CoRR abs/1005.1934 (2010)

Sameer Singh, Michael L. Wick, Andrew McCallum: Distantly Labeling Data for Large Scale Cross-Document Coreference. CoRR abs/1005.4298 (2010)

Charles A. Sutton, Andrew McCallum: Piecewise training for structured prediction. Machine Learning 77(2-3): 165-194 (2009)

Gregory Druck, Gideon S. Mann, Andrew McCallum: Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria. ACL/IJCNLP 2009: 360-368

Gregory Druck, Burr Settles, Andrew McCallum: Active Learning by Labeling Features. EMNLP 2009: 81-90

Kedar Bellare, Andrew McCallum: Generalized Expectation Criteria for Bootstrapping Extractors using Record-Text Alignment. EMNLP 2009: 131-140

David M. Mimno, Hanna M. Wallach, Jason Naradowsky, David A. Smith, Andrew McCallum: Polylingual Topic Models. EMNLP 2009: 880-889

Limin Yao, David M. Mimno, Andrew McCallum: Efficient methods for topic model inference on streaming document collections. KDD 2009: 937-946

Andrew McCallum, Karl Schultz, Sameer Singh: FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. NIPS 2009: 1249-1257

Hanna M. Wallach, David M. Mimno, Andrew McCallum: Rethinking LDA: Why Priors Matter. NIPS 2009: 1973-1981

Michael L. Wick, Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum: Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. NIPS 2009: 2044-2052

Sameer Singh, Karl Schultz, Andrew McCallum: Bi-directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs. ECML/PKDD (2) 2009: 414-429

Michael L. Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum: An Entity Based Model for Coreference Resolution. SDM 2009: 365-376

Kedar Bellare, Gregory Druck, Andrew McCallum: Alternating Projections for Learning with Expectation Constraints. UAI 2009: 43-50

Haizheng Zhang, Myra Spiliopoulou, Bamshad Mobasher, C. Lee Giles, Andrew McCallum, Olfa Nasraoui, Jaideep Srivastava, John Yen (Eds.): Advances in Web Mining and Web Usage Analysis, 9th International Workshop on Knowledge Discovery on the Web, WebKDD 2007, and 1st International Workshop on Social Networks Analysis, SNA-KDD 2007, San Jose, CA, USA, August 12-15, 2007. Revised Papers. Lecture Notes in Computer Science 5439, Springer 2009, isbn 978-3-642-00527-5

Gideon S. Mann, Andrew McCallum: Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields. ACL 2008: 870-878

Rebecca Reznik-Zellen, Bob Stevens, Michael Thorn, Jeff Morse, Mark D. Smucker, James Allan, David M. Mimno, Andrew McCallum, Mark Tuominen: InterNano: e-Science for the Nanomanufacturing Community. eScience 2008: 382-383

Robert Hall, Charles A. Sutton, Andrew McCallum: Unsupervised deduplication using cross-field dependencies. KDD 2008: 310-317

Michael L. Wick, Khashayar Rohanimanesh, Karl Schultz, Andrew McCallum: A unified approach for schema matching, coreference and canonicalization. KDD 2008: 722-730

Michael L. Wick, Khashayar Rohanimanesh, Andrew McCallum, AnHai Doan: A Discriminative Approach to Ontology Mapping. NTII 2008: 16-19

Gregory Druck, Gideon S. Mann, Andrew McCallum: Learning from labeled features using generalized expectation criteria. SIGIR 2008: 595-602

David M. Mimno, Andrew McCallum: Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. UAI 2008: 411-418

William W. Cohen, Andrew McCallum, Sam T. Roweis (Eds.): Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008. ACM International Conference Proceeding Series 307, ACM 2008, isbn 978-1-60558-205-4

Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel: Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. J. Artif. Intell. Res. (JAIR) 30: 249-272 (2007)

Charles A. Sutton, Andrew McCallum, Khashayar Rohanimanesh: Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. Journal of Machine Learning Research 8: 693-723 (2007)

Haizheng Zhang, John Yen, C. Lee Giles, Bamshad Mobasher, Myra Spiliopoulou, Jaideep Srivastava, Olfa Nasraoui, Andrew McCallum: WebKDD/SNAKDD 2007: web mining and social network analysis post-workshop report. SIGKDD Explorations 9(2): 87-92 (2007)

Pallika H. Kanani, Andrew McCallum: Resource-Bounded Information Gathering for Correlation Clustering. COLT 2007: 625-627

Vidit Jain, Erik G. Learned-Miller, Andrew McCallum: People-LDA: Anchoring Topics to People using Face Recognition. ICCV 2007: 1-8

Gary B. Huang, Erik G. Learned-Miller, Andrew McCallum: Cryptogram Decoding for OCR Using Numerization Strings. ICDAR 2007: 208-212

Xuerui Wang, Andrew McCallum, Xing Wei: Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval. ICDM 2007: 697-702

Gideon S. Mann, Andrew McCallum: Simple, robust, scalable semi-supervised learning via expectation regularization. ICML 2007: 593-600

David M. Mimno, Wei Li, Andrew McCallum: Mixtures of hierarchical topics with Pachinko allocation. ICML 2007: 633-640

Charles A. Sutton, Andrew McCallum: Piecewise pseudolikelihood for efficient training of conditional random fields. ICML 2007: 863-870

Pallika H. Kanani, Andrew McCallum, Chris Pal: Improving Author Coreference by Resource-Bounded Information Gathering from the Web. IJCAI 2007: 429-434

David M. Mimno, Andrew McCallum: Mining a digital library for influential authors. JCDL 2007: 105-106

David M. Mimno, Andrew McCallum: Organizing the OCA: learning faceted subjects from a library of digital books. JCDL 2007: 376-385

Aron Culotta, Michael L. Wick, Robert Hall, Matthew Marzilli, Andrew McCallum: Canonicalization of database records using adaptive similarity measures. KDD 2007: 201-209

Gregory Druck, Chris Pal, Andrew McCallum, Xiaojin Zhu: Semi-supervised classification with hybrid generative/discriminative methods. KDD 2007: 280-289

David M. Mimno, Andrew McCallum: Expertise modeling for matching papers with reviewers. KDD 2007: 500-509

Xuerui Wang, Chris Pal, Andrew McCallum: Generalized component analysis for text with heterogeneous attributes. KDD 2007: 794-803

Aron Culotta, Michael L. Wick, Andrew McCallum: First-Order Probabilistic Models for Coreference Resolution. HLT-NAACL 2007: 81-88

Gideon S. Mann, Andrew McCallum: Efficient Computation of Entropy Gradient for Semi-Supervised Conditional Random Fields. HLT-NAACL (Short Papers) 2007: 109-112

Wei Li, David M. Blei, Andrew McCallum: Nonparametric Bayes Pachinko Allocation. UAI 2007: 243-250

Charles A. Sutton, Andrew McCallum: Improved Dynamic Schedules for Belief Propagation. UAI 2007: 376-383

Aron Culotta, Trausti T. Kristjansson, Andrew McCallum, Paul A. Viola: Corrective feedback and persistent learning for information extraction. Artif. Intell. 170(14-15): 1101-1122 (2006)

Fuchun Peng, Andrew McCallum: Information extraction from research papers using conditional random fields. Inf. Process. Manage. 42(4): 963-979 (2006)

Xing Wei, W. Bruce Croft, Andrew McCallum: Table extraction for answer retrieval. Inf. Retr. 9(5): 589-611 (2006)

Andrew McCallum, Chris Pal, Gregory Druck, Xuerui Wang: Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification. AAAI 2006: 433-439

Shaolei Feng, R. Manmatha, Andrew McCallum: Exploring the Use of Conditional Random Field Models and HMMs for Historical Handwritten Document Recognition. DIAL 2006: 30-37

Michael L. Wick, Aron Culotta, Andrew McCallum: Learning Field Compatibilities to Extract Database Records from Unstructured Text. EMNLP 2006: 603-611

Wei Li, Andrew McCallum: Pachinko allocation: DAG-structured mixture models of topic correlations. ICML 2006: 577-584

B. Michael Kelm, Chris Pal, Andrew McCallum: Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning. ICPR (2) 2006: 828-832

Gideon S. Mann, David M. Mimno, Andrew McCallum: Bibliometric impact measures leveraging topic analysis. JCDL 2006: 65-74

Xuerui Wang, Andrew McCallum: Topics over time: a non-Markov continuous-time model of topical trends. KDD 2006: 424-433

Andrew McCallum: Information extraction, data mining and joint inference. KDD 2006: 835

Aron Culotta, Andrew McCallum, Jonathan Betz: Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text. HLT-NAACL 2006

Charles A. Sutton, Michael Sindelar, Andrew McCallum: Reducing Weight Undertraining in Structured Discriminative Learning. HLT-NAACL 2006

Andrew McCallum: Information extraction: distilling structured data from unstructured text. ACM Queue 3(9): 48-57 (2005)

Aron Culotta, Andrew McCallum: Reducing Labeling Effort for Structured Prediction Tasks. AAAI 2005: 746-751

Wei Li, Andrew McCallum: Semi-Supervised Sequence Modeling with Syntactic Topic Models. AAAI 2005: 813-818

Nadia Ghamrawi, Andrew McCallum: Collective multi-label classification. CIKM 2005: 195-200

Aron Culotta, Andrew McCallum: Joint deduplication of multiple record types in relational data. CIKM 2005: 257-258

Ron Bekkerman, Ran El-Yaniv, Andrew McCallum: Multi-way distributional clustering via pairwise interactions. ICML 2005: 41-48

Andrew McCallum, Andrés Corrada-Emmanuel, Xuerui Wang: Topic and Role Discovery in Social Networks. IJCAI 2005: 786-791

Yu Gu, Andrew McCallum, Donald F. Towsley: Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation. Internet Measurment Conference 2005: 345-350

Charles A. Sutton, Andrew McCallum: Composition of Conditional Random Fields for Transfer Learning. HLT/EMNLP 2005

Xuerui Wang, Natasha Mohanty, Andrew McCallum: Group and Topic Discovery from Relations and Their Attributes. NIPS 2005

Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira: A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance. UAI 2005: 388-395

Charles A. Sutton, Andrew McCallum: Piecewise Training for Undirected Models. UAI 2005: 568-575

Ron Bekkerman, Andrew McCallum: Disambiguating Web appearances of people in a social network. WWW 2005: 463-470

Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum: Interactive Information Extraction with Constrained Conditional Random Fields. AAAI 2004: 412-418

Aron Culotta, Ron Bekkerman, Andrew McCallum: Extracting social networks and contact information from email and the Web. CEAS 2004

Fuchun Peng, Fangfang Feng, Andrew McCallum: Chinese Segmentation and New Word Detection using Conditional Random Fields. COLING 2004

Charles A. Sutton, Khashayar Rohanimanesh, Andrew McCallum: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. ICML 2004

Fuchun Peng, Andrew McCallum: Accurate Information Extraction from Research Papers using Conditional Random Fields. HLT-NAACL 2004: 329-336

Andrew McCallum, Ben Wellner: Conditional Models of Identity Uncertainty with Application to Noun Coreference. NIPS 2004

Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay: An Integrated, Conditional Model of Information Extraction and Coreference with Appli. UAI 2004: 593-601

James Allan, Jay Aslam, Nicholas J. Belkin, Chris Buckley, James P. Callan, W. Bruce Croft, Susan T. Dumais, Norbert Fuhr, Donna Harman, David J. Harper, Djoerd Hiemstra, Thomas Hofmann, Eduard H. Hovy, Wessel Kraaij, John D. Lafferty, Victor Lavrenko, David D. Lewis, Liz Liddy, R. Manmatha, Andrew McCallum, Jay M. Ponte, John M. Prager, Dragomir R. Radev, Philip Resnik, Stephen E. Robertson, Ronald Rosenfeld, Salim Roukos, Mark Sanderson, Richard M. Schwartz, Amit Singhal, Alan F. Smeaton, Howard R. Turtle, Ellen M. Voorhees, Ralph M. Weischedel, Jinxi Xu, ChengXiang Zhai: Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002. SIGIR Forum 37(1): 31-47 (2003)

Wei Li, Andrew McCallum: Rapid development of Hindi named entity recognition using conditional random fields and feature induction. ACM Trans. Asian Lang. Inf. Process. 2(3): 290-294 (2003)

David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft: Table Extraction Using Conditional Random Fields. DG.O 2003

Andrew McCallum, Ben Wellner: Toward Conditional Models of Identity Uncertainty with Application to Proper Noun Coreference. IIWeb 2003: 79-84

Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum: Classification with Hybrid Generative/Discriminative Models. NIPS 2003

David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft: Table extraction using conditional random fields. SIGIR 2003: 235-242

Andrew McCallum: Efficiently Inducing Features of Conditional Random Fields. UAI 2003: 403-410

David M. Blei, J. Andrew Bagnell, Andrew McCallum: Learning with Scope, with Application to Information Extraction and Classification. UAI 2002: 53-60

John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML 2001: 282-289

Nicholas Roy, Andrew McCallum: Toward Optimal Active Learning through Sampling Estimation of Error Reduction. ICML 2001: 441-448

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)

William W. Cohen, Andrew McCallum, Dallan Quass: Learning to Understand the Web. IEEE Data Eng. Bull. 23(3): 17-24 (2000)

Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore: Automating the Construction of Internet Portals with Machine Learning. Inf. Retr. 3(2): 127-163 (2000)

Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39(2/3): 103-134 (2000)

Dayne Freitag, Andrew McCallum: Information Extraction with HMM Structures Learned by Stochastic Optimization. AAAI/IAAI 2000: 584-589

Huan Chang, David Cohn, Andrew McCallum: Learning to Create Customized Authority Lists. ICML 2000: 127-134

Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira: Maximum Entropy Markov Models for Information Extraction and Segmentation. ICML 2000: 591-598

Andrew McCallum, Kamal Nigam, Lyle H. Ungar: Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000: 169-178

Jason Rennie, Andrew McCallum: Using Reinforcement Learning to Spider the Web Efficiently. ICML 1999: 335-343

Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore: A Machine Learning Approach to Building Domain-Specific Search Engines. IJCAI 1999: 662-667

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

Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell: Learning to Classify Text from Labeled and Unlabeled Documents. AAAI/IAAI 1998: 792-799

Andrew McCallum, Kamal Nigam: Employing EM and Pool-Based Active Learning for Text Classification. ICML 1998: 350-358

Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng: Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998: 359-367

L. Douglas Baker, Andrew McCallum: Distributional Clustering of Words for Text Classification. SIGIR 1998: 96-103

Andrew McCallum: Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State. ICML 1995: 387-395

Andrew McCallum: Instance-Based State Identification for Reinforcement Learning. NIPS 1994: 377-384

Andrew McCallum: Overcoming Incomplete Perception with Util Distinction Memory. ICML 1993: 190-196

Andrew McCallum: Using Transitional Proximity for Faster Reinforcement Learning. ML 1992: 316-321

Andrew McCallum, Kent A. Spackman: Using Genetic Algorithms to Learn Disjunctive Rules from Examples. ML 1990: 149-152