Thomas Hofmann
- Research Area(s)
- Algorithms and Theory
- Machine Perception
Co-Authors
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
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
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
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
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
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
ICML (2000), pp. 351-358
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Learning probabilistic models of the Web
SIGIR (2000), pp. 369-371
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ProbMap - A probabilistic approach for mapping large document collections
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
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
NIPS (1999), pp. 914-920
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Probabilistic Latent Semantic Analysis
UAI (1999), pp. 289-296
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Probabilistic Latent Semantic Indexing
SIGIR (1999), pp. 50-57
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Probabilistic Topic Maps: Navigating through Large Text Collections
IDA (1999), pp. 161-172
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The Cluster-Abstraction Model: Unsupervised Learning of Topic Hierarchies from Text Data
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






