BibSLEIGH — Andrew

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: McCallum:Andrew

Facilitated 1 volumes:

Contributed to:

2011

2010

2009

2008

2007

2006

2005

2004

2003

2001

2000

1999

1998

1995

1993

1992

1990

2006

2007

2012

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.