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Travelled to:
1 × Australia
1 × Brazil
1 × China
1 × France
1 × Israel
1 × Singapore
1 × Slovenia
13 × USA
2 × Canada
2 × Germany
2 × United Kingdom
Collaborated with:
D.M.Mimno G.Druck C.A.Sutton G.S.Mann M.L.Wick C.Pal A.Culotta W.Li X.Wang K.Nigam K.Rohanimanesh F.C.N.Pereira R.Hall N.Ghamrawi N.Roy J.Rennie L.D.Baker K.A.Spackman G.Miklau L.Yao G.B.Huang E.G.Learned-Miller B.M.Kelm R.Bekkerman R.El-Yaniv J.D.Lafferty H.Chang D.Cohn D.Freitag L.H.Ungar K.Schultz X.Zhu D.Pinto X.Wei W.B.Croft R.Rosenfeld T.M.Mitchell A.Y.Ng A.Bakalov H.M.Wallach K.Bellare M.Marzilli
Talks about:
learn (12) model (9) use (9) topic (7) classif (6) condit (5) field (5) data (5) random (4) effici (4)

Person: Andrew McCallum

DBLP DBLP: McCallum:Andrew

Facilitated 1 volumes:

ICML 2008Ed

Contributed to:

CIKM 20112011
ICML 20112011
ICML 20102010
VLDB 20102010
KDD 20092009
KDD 20082008
SIGIR 20082008
ICDAR 20072007
ICML 20072007
KDD 20072007
ICML 20062006
ICPR v2 20062006
KDD 20062006
CIKM 20052005
ICML 20052005
ICML 20042004
SIGIR 20032003
ICML 20012001
ICML 20002000
KDD 20002000
ICML 19991999
ICML 19981998
SIGIR 19981998
ICML 19951995
ICML 19931993
ML 19921992
ML 19901990
JCDL 20062006
JCDL 20072007
JCDL 20122012

Wrote 42 papers:

CIKM-2011-DruckM #evaluation #interactive #towards
Toward interactive training and evaluation (GD, AM), pp. 947–956.
ICML-2011-WickRBCM #graph #named
SampleRank: Training Factor Graphs with Atomic Gradients (MLW, KR, KB, AC, AM), pp. 777–784.
ICML-2010-DruckM #generative #learning #modelling #using
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models (GD, AM), pp. 319–326.
VLDB-2010-WickMM #database #graph #probability #scalability
Scalable Probabilistic Databases with Factor Graphs and MCMC (MLW, AM, GM), pp. 794–804.
KDD-2009-YaoMM #documentation #model inference #performance #streaming #topic
Efficient methods for topic model inference on streaming document collections (LY, DMM, AM), pp. 937–946.
KDD-2008-HallSM #dependence #using
Unsupervised deduplication using cross-field dependencies (RH, CAS, AM), pp. 310–317.
KDD-2008-WickRSM #approach
A unified approach for schema matching, coreference and canonicalization (MLW, KR, KS, AM), pp. 722–730.
SIGIR-2008-DruckMM #learning #using
Learning from labeled features using generalized expectation criteria (GD, GSM, AM), pp. 595–602.
ICDAR-2007-HuangLM #string #using
Cryptogram Decoding for OCR Using Numerization Strings (GBH, EGLM, AM), pp. 208–212.
ICML-2007-MannM #learning #robust #scalability
Simple, robust, scalable semi-supervised learning via expectation regularization (GSM, AM), pp. 593–600.
ICML-2007-MimnoLM #topic
Mixtures of hierarchical topics with Pachinko allocation (DMM, WL, AM), pp. 633–640.
ICML-2007-SuttonM #performance #pseudo #random
Piecewise pseudolikelihood for efficient training of conditional random fields (CAS, AM), pp. 863–870.
KDD-2007-CulottaWHMM #adaptation #database #metric #similarity #using
Canonicalization of database records using adaptive similarity measures (AC, MLW, RH, MM, AM), pp. 201–209.
KDD-2007-DruckPMZ #classification #generative #hybrid
Semi-supervised classification with hybrid generative/discriminative methods (GD, CP, AM, XZ), pp. 280–289.
KDD-2007-MimnoM #modelling
Expertise modeling for matching papers with reviewers (DMM, AM), pp. 500–509.
KDD-2007-WangPM #analysis #component
Generalized component analysis for text with heterogeneous attributes (XW, CP, AM), pp. 794–803.
ICML-2006-LiM #correlation #modelling #topic
Pachinko allocation: DAG-structured mixture models of topic correlations (WL, AM), pp. 577–584.
ICPR-v2-2006-KelmPM #classification #generative #learning #multi
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning (BMK, CP, AM), pp. 828–832.
KDD-2006-McCallum #data mining #information management #mining
Information extraction, data mining and joint inference (AM), p. 835.
KDD-2006-WangM #roadmap #topic
Topics over time: a non-Markov continuous-time model of topical trends (XW, AM), pp. 424–433.
CIKM-2005-CulottaM #multi #relational
Joint deduplication of multiple record types in relational data (AC, AM), pp. 257–258.
CIKM-2005-GhamrawiM #classification #multi
Collective multi-label classification (NG, AM), pp. 195–200.
ICML-2005-BekkermanEM #clustering #interactive #multi
Multi-way distributional clustering via pairwise interactions (RB, REY, AM), pp. 41–48.
ICML-2004-SuttonRM #modelling #probability #random #sequence
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data (CAS, KR, AM).
SIGIR-2003-PintoMWC #random #using
Table extraction using conditional random fields (DP, AM, XW, WBC), pp. 235–242.
ICML-2001-LaffertyMP #modelling #probability #random #sequence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data (JDL, AM, FCNP), pp. 282–289.
ICML-2001-RoyM #estimation #fault #learning #reduction #towards
Toward Optimal Active Learning through Sampling Estimation of Error Reduction (NR, AM), pp. 441–448.
ICML-2000-ChangCM #learning
Learning to Create Customized Authority Lists (HC, DC, AM), pp. 127–134.
ICML-2000-McCallumFP #information management #markov #modelling #segmentation
Maximum Entropy Markov Models for Information Extraction and Segmentation (AM, DF, FCNP), pp. 591–598.
KDD-2000-McCallumNU #clustering #performance #set
Efficient clustering of high-dimensional data sets with application to reference matching (AM, KN, LHU), pp. 169–178.
ICML-1999-RennieM #learning #using #web
Using Reinforcement Learning to Spider the Web Efficiently (JR, AM), pp. 335–343.
ICML-1998-McCallumN #classification #learning
Employing EM and Pool-Based Active Learning for Text Classification (AM, KN), pp. 350–358.
ICML-1998-McCallumRMN #classification
Improving Text Classification by Shrinkage in a Hierarchy of Classes (AM, RR, TMM, AYN), pp. 359–367.
SIGIR-1998-BakerM #classification #clustering #word
Distributional Clustering of Words for Text Classification (LDB, AM), pp. 96–103.
ICML-1995-McCallum #learning
Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State (AM), pp. 387–395.
ICML-1993-McCallum #memory management
Overcoming Incomplete Perception with Util Distinction Memory (AM), pp. 190–196.
ML-1992-McCallum #learning #performance #proximity #using
Using Transitional Proximity for Faster Reinforcement Learning (AM), pp. 316–321.
ML-1990-McCallumS #algorithm #search-based #using
Using Genetic Algorithms to Learn Disjunctive Rules from Examples (AM, KAS), pp. 149–152.
JCDL-2006-MannMM #analysis #metric #topic
Bibliometric impact measures leveraging topic analysis (GSM, DMM, AM), pp. 65–74.
JCDL-2007-MimnoM #library #mining
Mining a digital library for influential authors (DMM, AM), pp. 105–106.
JCDL-2007-MimnoM07a #learning #library
Organizing the OCA: learning faceted subjects from a library of digital books (DMM, AM), pp. 376–385.
JCDL-2012-BakalovMWM #modelling #taxonomy #topic
Topic models for taxonomies (AB, AM, HMW, DMM), pp. 237–240.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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