Thomas Hofmann

Google Publications

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    Beyond Sliding Windows: Object Localization by Efficient Subwindow Search

    Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann

    IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, AK (2008)

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    Robust Collaborative Filtering

    Bhaskar Mehta, Thomas Hofmann, Wolfgang Nejdl

    ACM Conference on Recommender Systems, ACM, Minneapolis, MN (2007), pp. 49-56

Previous Publications

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    Exponential Families for Conditional Random Fields

    Yasemin Altun, Alexander J. Smola, Thomas Hofmann

    CoRR, vol. abs/1207.4131 (2012)

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    A brain computer interface with online feedback based on magnetoencephalography

    Thomas Navin Lal, Michael Schroeder, N. Jeremy Hill, Hubert Preiss, Thilo Hinterberger, J"rgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas Hofmann, Niels Birbaumer, Bernhard Schoelkopf

    ICML (2005), pp. 465-472

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    Collaborative Machine Learning

    Thomas Hofmann, Justin Basilico

    From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments (2005), pp. 173-182

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    From bits and bytes to information and knowledge

    Thomas Hofmann

    CIKM (2005), pp. 3

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    Non-redundant clustering with conditional ensembles

    David Gondek, Thomas Hofmann

    KDD (2005), pp. 70-77

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    A joint framework for collaborative and content filtering

    Justin Basilico, Thomas Hofmann

    SIGIR (2004), pp. 550-551

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    Exponential Families for Conditional Random Fields

    Yasemin Altun, Alexander J. Smola, Thomas Hofmann

    UAI (2004), pp. 2-9

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    Gaussian process classification for segmenting and annotating sequences

    Yasemin Altun, Thomas Hofmann, Alex J. Smola

    ICML (2004)

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    Hierarchical document categorization with support vector machines

    Lijuan Cai, Thomas Hofmann

    CIKM (2004), pp. 78-87

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    Latent semantic models for collaborative filtering

    Thomas Hofmann

    ACM Trans. Inf. Syst., vol. 22 (2004), pp. 89-115

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    Learning Over Compact Metric Spaces

    H. Quang Minh, Thomas Hofmann

    COLT (2004), pp. 239-254

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    Non-Redundant Data Clustering

    David Gondek, Thomas Hofmann

    ICDM (2004), pp. 75-82

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    Semi-supervised Learning on Directed Graphs

    Dengyong Zhou, Bernhard Sch"lkopf, Thomas Hofmann

    NIPS (2004)

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    Support vector machine learning for interdependent and structured output spaces

    Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun

    ICML (2004)

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    Unifying collaborative and content-based filtering

    Justin Basilico, Thomas Hofmann

    ICML (2004)

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    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

    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, Rich Schwartz, Amit Singhal, Alan F. Smeaton, Howard R. Turtle, Ellen M. Voorhees, Ralph M. Weischedel, Jinxi Xu, ChengXiang Zhai

    SIGIR Forum, vol. 37 (2003), pp. 31-47

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    Collaborative filtering via gaussian probabilistic latent semantic analysis

    Thomas Hofmann

    SIGIR (2003), pp. 259-266

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    Hidden Markov Support Vector Machines

    Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann

    ICML (2003), pp. 3-10

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    Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge

    Massimiliano Ciaramita, Thomas Hofmann, Mark Johnson

    IJCAI (2003), pp. 817-822

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    Multiple-Instance Learning via Disjunctive Programming Boosting

    Stuart Andrews, Thomas Hofmann

    NIPS (2003)

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    Text categorization by boosting automatically extracted concepts

    Lijuan Cai, Thomas Hofmann

    SIGIR (2003), pp. 182-189

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    Discriminative Learning for Label Sequences via Boosting

    Yasemin Altun, Thomas Hofmann, Mark Johnson

    NIPS (2002), pp. 977-984

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    Multiple Instance Learning with Generalized Support Vector Machines

    Stuart Andrews, Thomas Hofmann, Ioannis Tsochantaridis

    AAAI/IAAI (2002), pp. 943-944

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    Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms

    Scott Doniger, Thomas Hofmann, Miao-Hui Joanne Yeh

    Journal of Computational Biology, vol. 9 (2002), pp. 849

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    Support Vector Machines for Multiple-Instance Learning

    Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann

    NIPS (2002), pp. 561-568

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    Support Vector Machines for Polycategorical Classification

    Ioannis Tsochantaridis, Thomas Hofmann

    ECML (2002), pp. 456-467

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    Learning What People (Don't) Want

    Thomas Hofmann

    ECML (2001), pp. 214-225

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    Text Classification in a Hierarchical Mixture Model for Small Training Sets

    Kristina Toutanova, Francine Chen, Kris Popat, Thomas Hofmann

    CIKM (2001), pp. 105-112

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    Unsupervised Learning by Probabilistic Latent Semantic Analysis

    Thomas Hofmann

    Machine Learning, vol. 42 (2001), pp. 177-196

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    A theory of proximity based clustering: structure detection by optimization

    Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann

    Pattern Recognition, vol. 33 (2000), pp. 617-634

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    Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval

    Keith B. Hall, Thomas Hofmann

    ICML (2000), pp. 351-358

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    Learning probabilistic models of the Web

    Thomas Hofmann

    SIGIR (2000), pp. 369-371

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    ProbMap - A probabilistic approach for mapping large document collections

    Thomas Hofmann

    Intell. Data Anal., vol. 4 (2000), pp. 149-164

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    The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

    David A. Cohn, Thomas Hofmann

    NIPS (2000), pp. 430-436

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    Histogram Clustering for Unsupervised Image Segmentation

    Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann

    CVPR (1999), pp. 2602-2608

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    Histogram clustering for unsupervised segmentation and image retrieval

    Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann

    Pattern Recognition Letters, vol. 20 (1999), pp. 899-909

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    Latent Class Models for Collaborative Filtering

    Thomas Hofmann, Jan Puzicha

    IJCAI (1999), pp. 688-693

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    Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization

    Thomas Hofmann

    NIPS (1999), pp. 914-920

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    Probabilistic Latent Semantic Analysis

    Thomas Hofmann

    UAI (1999), pp. 289-296

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    Probabilistic Latent Semantic Indexing

    Thomas Hofmann

    SIGIR (1999), pp. 50-57

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    Probabilistic Topic Maps: Navigating through Large Text Collections

    Thomas Hofmann

    IDA (1999), pp. 161-172

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    The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data

    Thomas Hofmann

    IJCAI (1999), pp. 682-687

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    Discrete Mixture Models for Unsupervised Image Segmentation

    Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann

    DAGM-Symposium (1998), pp. 135-142

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    Learning from Dyadic Data

    Thomas Hofmann, Jan Puzicha, Michael I. Jordan

    NIPS (1998), pp. 466-472

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    Unsupervised Texture Segmentation in a Deterministic Annealing Framework

    Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann

    IEEE Trans. Pattern Anal. Mach. Intell., vol. 20 (1998), pp. 803-818

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    Active Data Clustering

    Thomas Hofmann, Joachim M. Buhmann

    NIPS (1997)

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    An Optimization Approach to Unsupervised Hierarchical Texture Segmentation

    Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann

    ICIP (3) (1997), pp. 213-216

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    Correction to "Pairwise Data Clustering by Deterministic Annealing"

    Thomas Hofmann, Joachim M. Buhmann

    IEEE Trans. Pattern Anal. Mach. Intell., vol. 19 (1997), pp. 192

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    Deterministic Annealing for Unsupervised Texture Segmentation

    Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann

    EMMCVPR (1997), pp. 213-228

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    Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval

    Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann

    CVPR (1997), pp. 267-272

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    Pairwise Data Clustering by Deterministic Annealing

    Thomas Hofmann, Joachim M. Buhmann

    IEEE Trans. Pattern Anal. Mach. Intell., vol. 19 (1997), pp. 1-14

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    An Annealed ``Neural Gas'' Network for Robust Vector Quantization

    Thomas Hofmann, Joachim M. Buhmann

    ICANN (1996), pp. 151-156

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    Inferring Hierarchical Clustering Structures by Deterministic Annealing

    Thomas Hofmann, Joachim M. Buhmann

    KDD (1996), pp. 363-366

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    The Mobile Robot RHINO

    Joachim M. Buhmann, Wolfram Burgard, Armin B. Cremers, Dieter Fox, Thomas Hofmann, Frank E. Schneider, Jiannis Strikos, Sebastian Thrun

    AI Magazine, vol. 16 (1995), pp. 31-38

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    Multidimensional Scaling and Data Clustering

    Thomas Hofmann, Joachim M. Buhmann

    NIPS (1994), pp. 459-466

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    Central and Pairwise Data Clustering by Competitive Neural Networks

    Joachim M. Buhmann, Thomas Hofmann

    NIPS (1993), pp. 104-111