Home Page of Thorsten Joachims
        Picture of Thorsten Joachims


eMail: tj@cs.cornell.edu
Phone: (607)255-1372
Fax: (607)255-4428
Address: 4153 Upson Hall, Ithaca, NY 14853-7501

 

Administrative Assistant: Melissa Totman

 

Office Hours: Thursdays 1:30pm - 2:30pm 
 

Bio

Thorsten Joachims is an Associate Professor in the Department of Computer Science at Cornell University. He joined the department in 2001 after finishing his Ph. D. as a student of Prof. Morik at the AI-unit of the University of Dortmund, from where he also received a Diplom in Computer Science in 1997. Between 2000 and 2001 he worked as a PostDoc at the GMD in the Knowledge Discovery Team of the Institute for Autonomous Intelligent Systems. From 1994 to 1996 he spent one and a half years at Carnegie Mellon University as a visiting scholar of Prof. Tom Mitchell.  

Research Topics

·        Machine Learning, Support Vector Machines, Statistical Learning Theory

·        Text Classification, Text Mining, Web Mining, Information Retrieval

·         Intelligent Information Systems

Projects and Research

·        SVM-struct - a software package for predicting complex outputs (e.g. trees, alignments) with Support Vector Machines 

·        SVM-light - a software package for Support Vector Learning 

·        Spectral Graph Transducer (SGT) – software for transductive learning via spectral graph partitioning

·        NSF Project: Discriminative Methods for Learning with Dependent Outputs

·        NSF Project: Learning Structure to Structure Mappings

·        NSF Career Award: Learning Retrieval Functions from Implicit Feedback – Osmot search engine

·        WebWatcher  - a tour guide for the World Wide Web.

·        LASER - a retrieval engine for the Web that learns

Teaching

·         CS478 Machine Learning, Spring 2008, (Spring 2007, Spring 2006, Spring 2005, Spring 2004).

·         CS472/473 Foundations of Artificial Intelligence, Fall 2007, (Fall 2005).

·         CS778 Topics in Machine Learning: Learning to Predict Structured Objects, Fall 2006.

·         ENGRG150 Engineering Freshman Seminar, Fall 2006, (Fall 2004).

·         CS630 Representing and Accessing Digital Information, Fall 2004, (Fall 2003).

·        CS574 Language Technologies, with Claire Cardie, Fall 2002.

·        CS678 Advanced Topics in Machine Learning, Spring 2003, (with Rich Caruana, Spring 2002).
 

Ph.D. Students

·        Thomas Finley

·        Filip Radlinski

·        Benyah Shaparenko

·        Chun-Nam Yu

·        Yisong Yue

Books

·        T. Joachims, Learning to Classify Text using Support Vector Machines, Kluwer/Springer, 2002. [B&N] [Amazon] [Kluwer/Springer] [BibTeX]

Cornell

·        Machine Learning at Cornell

·        Artificial Intelligence at Cornell

·        CS772 AI Seminar

·        CS775 NLP Seminar
 

Editing

·        Beyond Binary Relevance, P. Bennett, B. Carterette, O. Chapelle, T. Joachims, SIGIR Workshop, 2008.

·        Machine Learning for Web Search, D. Zhou, O. Chapelle, T. Joachims, T. Hofmann, NIPS Workshop, 2007.

·        Learning to Rank for Information Retrieval, T. Joachims, Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, SIGIR Workshop, 2007.

·        Journal of Machine Learning Research (JMLR) (action editor).

·        Journal of Artificial Intelligence Research (JAIR) (associate editor).

·        Data Mining and Knowledge Discovery Journal (action editor).

·        Learning in Structured Output Spaces, U. Brefeld, T. Joachims, B. Taskar, E. Xing, ICML Workshop, 2006.

·        KDD-Cup 2004 – optimizing predictions for different performance measures (with R. Caruana).

·        Implicit Measures of User Interests and Preferences, S. Dumais, K. Bharat, T. Joachims, A. Weigend, SIGIR Workshop, 2003.

·        Special Issue on Text-Mining, Zeitschrift Künstliche Intelligenz, Vol. 2, 2002.

·        Special Issue on Automated Text Categorization, Journal on Intelligent Information Systems, T. Joachims and F. Sebastiani, Kluwer, Vol. 2, 2002. 

·        Beyond Classification and Regression: Learning Rankings, Preferences, Equality Predicates, and Other Structures  R. Caruana and T. Joachims, NIPS Workshop, 2002.

·        Machine Learning for Information Filtering. T. Joachims and A. McCallum and M. Sahami and M. Craven (ed.), IJCAI Workshop, AAAI Press, 1999.

·        Learning for Text Categorization. M. Sahami and M. Craven and T. Joachims and A. McCallum (ed.), AAAI/ICML  Workshop, WS-98-05, AAAI Press, 1998.

Publications

2008

  
[Joachims/etal/07b] T. Joachims and T. Finley and Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, to appear.
 [Draft PDF] [BibTeX] [Software]
[Finley/Joachims/08a] T. Finley and T. Joachims, Training Structural SVMs when Exact Inference is Intractable, Proceedings of the International Conference on Machine Learning (ICML), 2008.
[PDF] [BibTeX] 
[Yue/Joachims/08a] Yisong Yue and T. Joachims, Predicting Diverse Subsets Using Structural SVMs, Proceedings of the International Conference on Machine Learning (ICML), 2008.
[PDF] [BibTeX] [Software]
[Radlinski/etal/08a] F. Radlinski and R. Kleinberg and T. Joachims, Learning Diverse Rankings with Multi-Armed Bandits, Proceedings of the International Conference on Machine Learning (ICML), 2008.
[PDF] [BibTeX] 

2007

  
[Joachims/Radlinski/07a] T. Joachims, F. Radlinski, Search Engines that Learn from Implicit Feedback, IEEE Computer, Vol. 40, No. 8, August, 2007.
 [IEEE Digital Library] [BibTeX] [Software]
[Shaparenko/Joachims/07a] B. Shaparenko, T. Joachims, Information Genealogy: Uncovering the Flow of Ideas in Non-Hyperlinked Document Databases, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2007.
[PDF] [BibTeX] 
[Radlinski/Joachims/07a] F. Radlinski, T. Joachims, Active Exploration for Learning Rankings from Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2007.
[PDF] [BibTeX]
[Finley/Joachims/07a] T. Finley, T. Joachims, Parameter Learning for Loopy Markov Random Fields with Structural Support Vector Machines, ICML Workshop on Constrained Optimization and Structured Output Spaces, 2007.
[PDF] [BibTeX] [Software] 
[Yue/etal/07a] Yisong Yue, T. Finley, F. Radlinski, T. Joachims, A Support Vector Method for Optimizing Average Precision, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2007.
[PDF] [BibTeX] [Software] 
[Yu/etal/07a] Chun-Nam Yu, T. Joachims, R. Elber, J. Pillardy, Support Vector Training of Protein Alignment Models, Proceeding of the International Conference on Research in Computational Molecular Biology (RECOMB), 2007.
[PDF] [BibTeX] [Software] 
[Pohl/etal/07a] S. Pohl, F. Radlinski, T. Joachims, Recommending Related Papers Based on Digital Library Access Records, Proceeding of the Joint Conference on Digital Libraries (JCDL), 2007.
[PDF] [BibTeX]
[Joachims/etal/07a] T. Joachims, L. Granka, Bing Pan, H. Hembrooke, F. Radlinski, G. Gay, Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search, ACM Transactions on Information Systems (TOIS), Vol. 25, No. 2 (April), 2007.
[PDF] [BibTeX]
[Domshlak/Joachims/07a] C. Domshlak and T. Joachims, Efficient and Non-Parametric Reasoning over User Preferences, User Modeling and User-Adapted Interaction (UMUAI), Vol. 17, No. 1-2, pp. 41-69, Springer, 2007.
[Springer Link] [BibTeX]

2006

  
[Joachims/06a]
 Best Research Paper Award		 T. Joachims, Training Linear SVMs in Linear Time, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2006.
[Postscript] [PDF] [BibTeX] [Software] 
[Radlinski/Joachims/06a] F. Radlinski and T. Joachims, Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs, Proceedings of the National Conference of the American Association for Artificial Intelligence (AAAI), 2005.
[PDF] [BibTeX] [Software]
[Yu/etal/06a] Chun-Nam Yu, T. Joachims, and R. Elber, Training Protein Threading Models Using Structural SVMs, ICML Workshop on Learning in Structured Output Spaces, 2006.
[PDF] [BibTeX]

2005

  
[Shaparenko/etal/05a]
B. Shaparenko, R. Caruana, J. Gehrke, and T. Joachims, Identifying Temporal Patterns and Key Players in Document Collections. Proceedings of the IEEE ICDM Workshop on Temporal Data Mining: Algorithms, Theory and Applications (TDM-05), pp. 165–174, 2005.
 [PDF] [BibTeX]
[Joachims/05a]
 Best Paper Award		 T. Joachims, A Support Vector Method for Multivariate Performance Measures, Proceedings of the International Conference on Machine Learning (ICML), 2005.
 [Postscript] [PDF] [BibTeX] [Software] 
[Joachims/Hopcroft/05a] T. Joachims and J. Hopcroft, Error Bounds for Correlation Clustering, Proceedings of the International Conference on Machine Learning (ICML), 2005.
 [Postscript] [PDF] [BibTeX]
[Finley/Joachims/05a]
 Outstanding Student Paper Award		 T. Finley and T. Joachims, Supervised Clustering with Support Vector Machines, Proceedings of the International Conference on Machine Learning (ICML), 2005.
 [Postscript] [PDF] [BibTeX]
[Joachims/etal/05a] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, Accurately Interpreting Clickthrough Data as Implicit Feedback, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2005.
 [Postscript] [PDF] [BibTeX]
[Radlinski/Joachims/05a]
 Best Student Paper Award		 F. Radlinski and T. Joachims, Query Chains: Learning to Rank from Implicit Feedback, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2005.
[Postscript] [PDF] [BibTeX] [Software] 

[Radlinski/Joachims/05b]

F. Radlinski and T. Joachims, Evaluating the Robustness of Learning from Implicit Feedback, ICML Workshop on Learning In Web Search, 2005.
[Postscript] [PDF] [BibTeX]
[Domshlak/Joachims/05a] C. Domshlak and T. Joachims, Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2005.
[Postscript] [PDF] [BibTeX]
[Joachims/etal/05b] T. Joachims, T. Galor, and R. Elber, Learning to Align Sequences: A Maximum-Margin Approach, In: New Algorithms for Macromolecular Simulation, B. Leimkuhler, LNCS Vol. 49, Springer, 2005.
 [PDF] [BibTeX]
[Tsochantaridis/etal/05a] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large Margin Methods for Structured and Interdependent Output Variables, Journal of Machine Learning Research (JMLR), 6(Sep):1453-1484, 2005.
[PDF] [BibTeX] [Software]

2004

  

[Tsochantaridis/etal/04a]

I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, Support Vector Machine Learning for Interdependent and Structured Output Spaces, Proceedings of the International Conference on Machine Learning (ICML), 2004.
 [Postscript] [PDF] [BibTeX] [Software] 
[Granka/etal/04a] L. Granka, T. Joachims, and G. Gay, Eye-Tracking Analysis of User Behavior in WWW-Search, Poster Abstract, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2004.
 [PDF] [BibTeX]
[Caruana/etal/04a] R. Caruana, T. Joachims, and L. Backstrom. KDDCup 2004: Results and Analysis, ACM SIGKDD Newsletter, 6(2):95-108, 2004.
[PDF] [BibTeX]

[Ginsparg/etal/04a]

P. Ginsparg, P. Houle, T. Joachims, and J.-H. Sul, Mapping Subsets of Scholarly Information, Proceedings of the National Academy of Sciences of the USA, 10.1073, Vol. 101, pages 5236-5240, 2004.
 [BibTeX]

2003

  

[Schultz/Joachims/03a]

M. Schultz and T. Joachims, Learning a Distance Metric from Relative Comparisons, Proceedings of the Conference on Advance in Neural Information Processing Systems (NIPS), 2003.
 [Postscript] [PDF] [BibTeX]
[Joachims/03a] T. Joachims, Transductive Learning via Spectral Graph Partitioning, Proceedings of the International Conference on Machine Learning (ICML), 2003.
 [Postscript] [PDF] [BibTeX] [Software]

[Joachims/03b]

T. Joachims, Learning to Align Sequences: A Maximum-Margin Approach, Technical Report, August, 2003.
 [Postscript] [PDF] [BibTeX]
[Joachims/03c] T. Joachims, Evaluating Retrieval Performance Using Clickthrough Data, in J. Franke and G. Nakhaeizadeh and I. Renz, "Text Mining", Physica/Springer Verlag, pp. 79-96, 2003.

2002

  

[Joachims/02a]

T. Joachims, Learning to Classify Text using Support Vector Machines, Dissertation, Kluwer, 2002.
[Abstract] [B&N] [Amazon] [Kluwer] [BibTeX] [Software]

[Joachims/02b]

T. Joachims, Evaluating Retrieval Performance Using Clickthrough Data, Proceedings of the SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, 2002.
 [Postscript] [PDF] [BibTeX]

[Joachims/02c]

T. Joachims, Optimizing Search Engines Using Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002.
 [Postscript] [PDF] [BibTeX] [Software]

[Joachims/02d]

T. Joachims, The Maximum-Margin Approach to Learning Text Classifiers, Ausgezeichnete Informatikdissertationen 2001, D. Wagner et al. (Hrsg.), GI-Edition - Lecture Notes in Informatics (LNI), Köllen Verlag, Bonn, 2002.

[Sengers/etal/02a]

P. Sengers, R. Liesendahl, W. Magar, C. Seibert, B. Mueller, T. Joachims, W. Geng, P. Martensson, and K. Hook, The Enigmatics of Affect, Proceedings of the Conference on Designing Interactive Systems (DIS), 2002.

2001

  

[Wrobel/etal/01a]

S. Wrobel, K. Morik, and T. Joachims, Maschinelles Lernen und Data Mining in: G. Görz, C. Rollinger, J. Schneeberger, Handbuch der künstlichen Intelligenz, Oldenburg, 2001.

[Joachims/01a]

T. Joachims, A Statistical Learning Model of Text Classification with Support Vector Machines. Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2001.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/etal/01a]

T. Joachims, N. Cristianini, and J. Shawe-Taylor, Composite Kernels for Hypertext Categorisation, Proceedings of the International Conference on Machine Learning (ICML), 2001.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/01b]

T. Joachims, The Web as the Bias. Poster at the Learning Workshop in Snowbird, 2001.

[Morik/etal/01a]

K. Morik, T. Joachims, M. Imhoff, P. Brockhausen, and S. Rueping, Integrating Kernel Methods into a Knowledge-Based Approach to Evidence-Based Medicine. In: L. Jain, Computational Intelligence Techniques in Medical Diagnosis and Prognosis, 2001.

2000

  

[Joachims/00a]

T. Joachims, Estimating the Generalization Performance of a SVM Efficiently. Proceedings of the International Conference on Machine Learning (ICML), Morgan Kaufman, 2000.
 [Postscript (gz)] [PDF] [BibTeX] [Software]

[Klinkenberg/Joachims/00a]

R. Klinkenberg and T. Joachims, Detecting Concept Drift with Support Vector Machines. Proceedings of the International Conference on Machine Learning (ICML), Morgan Kaufmann, 2000.
 [Postscript (gz)] [PDF (gz)] [BibTeX]

[Morik/etal/00a]

K. Morik, M. Imhoff, P. Brockhausen, T. Joachims, and U. Gather, Knowledge Discovery and Knowledge Validation in Intensive Care. Artificial Intelligence in Medicine, 2001.
[Elsevier] [BibTeX] 

1999

  

[Joachims/99a]

T. Joachims, Making Large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola (ed.), MIT Press, 1999.
 [Postscript (gz)] [PDF] [BibTeX] [Software]

[Joachims/99b]

T. Joachims, Wissenserlangung aus grossen Datenbanken. 9th Int. Symposium on Intensive Care, W.Kuckelt and K.Hankeln (ed.), Journal f. Anaesthesie und Intensivbehandlung, Pabst Science Publishers, 1999.

[Joachims/99c]

T. Joachims, Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the International Conference on Machine Learning (ICML), 1999.
 [Postscript (gz)] [PDF] [BibTeX] [Software]

[Joachims/99d]

T. Joachims, Aktuelles Schlagwort: Support Vector Machines. Künstliche Intelligenz, Vol. 4, 1999.
[BibTeX]

[Joachims/99e]

T. Joachims, Estimating the Generalization Performance of a SVM Efficiently. LS8-Report 25, Universität Dortmund, LS VIII, 1999.
[Postscript (gz)] [BibTeX] [Software]

[Morik/etal/99a]

K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning with a knowledge-based approach - A case study in intensive care monitoring. Proceedings of the International Conference on Machine Learning (ICML), 1999.
 [Postscript (gz)] [PDF] [BibTeX]

[Scheffer/Joachims/99a]

Tobias Scheffer and Thorsten Joachims, Expected Error Analysis for Model Selection. Proceedings of the International Conference on Machine Learning (ICML), 1999.
 [BibTeX]

1998

  

[Armstrong/etal/98a]

Armstrong, Robert and Freitag, Dayne and Joachims, Thorsten and Mitchell, Tom, WebWatcher: A Learning Apprentice for the World Wide Web. Machine Learning and Data Mining, R. Michalski and I. Bratko and M. Kubat (ed.), Wiley, 1998, The file is a copy of Armstrong/etal/95a. Armstrong/etal/98a is a reprint of the 95a document.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/Mladenic/98a]

T. Joachims and D. Mladenic, Browsing-Assistenten, Tour Guides und adaptive WWW-Server. Künstliche Intelligenz, Vol. 3 (28), 1998.
[BibTeX]

[Joachims/98a]

T. Joachims, Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of the European Conference on Machine Learning (ECML), Springer, 1998.
 [Postscript (gz)] [PDF] [BibTeX] [Software]

[Joachims/98c]

Thorsten Joachims, Making large-Scale SVM Learning Practical. LS8-Report 24, Universität Dortmund, LS VIII-Report, 1998.
[Postscript (gz)] [PDF] [BibTeX] [Software]

[Scheffer/Joachims/98a]

Tobias Scheffer and Thorsten Joachims, Estimating the expected error of empirical minimizers for model selection. TR-98-9, TU-Berlin, 1998.
 [Postscript] [BibTeX]

1997

  

[Joachims/etal/97b]

Joachims, Thorsten and Freitag, Dayne and Mitchell, Tom, WebWatcher: A Tour Guide for the World Wide Web. Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Morgan Kaufmann, 1997.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/97a]

Joachims, Thorsten, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proceedings of International Conference on Machine Learning (ICML), 1997.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/97b]

T. Joachims, Text Categorization with Support Vector Machines: Learning with Many Relevant Features. LS8-Report 23, Universität Dortmund, LS VIII-Report, 1997.
[Postscript (gz)] [PDF] [BibTeX]

1996

  

[Boyan/etal/96a]

J. Boyan and D. Freitag and T. Joachims, A Machine Learning Architecture for Optimizing Web Search Engines. Proceedings of the AAAI Workshop on Internet Based Information Systems, 1996.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/96a]

Joachims, Thorsten, Einsatz eines intelligenten, lernenden Agenten für das World Wide Web. Fachbereich Informatik, Universität Dortmund, Diplomarbeit, 1996.
[Postscript (gz)] [PDF] [BibTeX]

1995

  

[Armstrong/etal/95a]

Armstrong, Robert and Freitag, Dayne and Joachims, Thorsten and Mitchell, Tom, WebWatcher: A Learning Apprentice for the World Wide Web. Proceedings of the 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, 1995.
 [Postscript (gz)] [PDF] [BibTeX]

[Joachims/etal/95a]

Joachims, Thorsten and Mitchell, Tom and Freitag, Dayne and Armstrong, Robert, WebWatcher: Machine Learning and Hypertext. Beiträge zum 7. Fachgruppentreffen MASCHINELLES LERNEN der GI-Fachgruppe 1.1.3, 1995, Forschungsbericht Nr. 580 der Universität Dortmund.
[Postscript (gz)] [PDF] [BibTeX]