Home Page of Thorsten Joachims

        Picture of Thorsten Joachims


eMail: 
tj@cs.cornell.edu
Phone: (607)255-1372
Fax: (607)255-4428
Address: 418 Gates Hall, Ithaca, NY 14853-7501

 

Office Hour: Tuesdays, 4:10pm - 5:10pm

 

Administrative Assistant: Amy Finch Elser

 

 
 

Bio

Thorsten Joachims is a Professor in the Department of Computer Science and in the Department of Information 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 (access from China)

·        SVM-light - a software package for Support Vector Learning (access from China)

·        Dynamic Ranking - software for Dynamic Ranked Retrieval

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

·        NSF Project: Learning to Model Sequences - Playlist Prediction for Local Music Discovery

·        NSF Project: Learning Structure to Structure Mappings

·        NSF Project: Learning from Implicit Feedback Through Online Experimentation

·        NSF Project: Information Genealogy

·        NSF Project: Discriminative Methods for Learning with Dependent Outputs

·        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

·         CS4780/5780 Machine Learning, Fall 2014.

·         CS6784 Advanced Topics in Machine Learning, Spring 2014, (Fall 2010).

·         CS4780/5780 Machine Learning, Fall 2013, (Fall 2012).

·         CS/ENGRD2110 Object-Oriented Programming and Data Structures, Spring 2012, (Spring 2011).

·        CS4780 Machine Learning, Fall 2009, (Spring 2008, Spring 2007, Spring 2006, Spring 2005, Spring 2004).

·         ENGRG1050 Engineering Advising Seminar, Fall 2009.

·         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 (Ph.D. 2008)

·        Filip Radlinski (Ph.D. 2008)

·        Benyah Shaparenko (Ph.D. 2009

·        Chun-Nam Yu (Ph.D. 2010)

·        Yisong Yue (Ph.D. 2010)

·        Shuo Chen

·        Joshua Moore

·        Karthik Raman

·        Tobias Schnabel

·        Ruben Sipos

·        Adith Swaminathan

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

·        CS7790 AI Seminar

·        CS7794 NLP Seminar

 

Editing

·        International Conference on Machine Learning (ICML), Program Chair (with Johannes Fuernkranz), 2010.

·        Journal of Machine Learning Research (JMLR) (action editor, 2004 - 2009).

·        Machine Learning Journal (MLJ) (action editor).

·        Journal of Artificial Intelligence Research (JAIR) (advisory board member).

·        Data Mining and Knowledge Discovery Journal (DMKD) (action editor, 2005 - 2008).

·        Special Issue on Learning to Rank for IR, Information Retrieval Journal, Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, T. Joachims, Springer, 2009.

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

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

·        Enriching Information Retrieval, P. Bennett, K. El-Arini, T. Joachims,  K. Svore, SIGIR Workshop, 2011.

·        Redundancy, Diversity, and Interdependent Document Relevance (IDR), P. Bennett, B. Carterette, T. Joachims, F. Radlinski, SIGIR Workshop, 2009.

·        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.

·        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.

·        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

2014

 
[Joachims/14a] T. Joachims, Learning from Rational Behavior, EMNLP Keynote Talk, 2014.
[Slides]
[Raman/Joachims/14a] K. Raman, T. Joachims, Methods for Ordinal Peer Grading, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2014.
[PDF] [Online Peergrading Service] [Software] [BibTeX]
[Ailon/etal/14a] N. Ailon, Z. Karnin, T. Joachims, Reducing Dueling Bandits to Cardinal Bandits, International Conference on Machine Learning (ICML), 2014.
[PDF] [BibTeX]
[Sipos/etal/14a] R. Sipos, A. Ghosh, T. Joachims, Was This Review Helpful to You? It Depends! Context and Voting Patterns in Online Content, International World Wide Web Conference (WWW), 2014.
[PDF] [BibTeX]
[Turnbull/etal/14a] D. Turnbull, J. Zupnick, K. Stensland, A. Horwitz, A. Wolf, A. Spirgel, S. Meyerhofer, T. Joachims, Using Personalized Radio to Enhance Local Music Discovery, Work in Progress Paper at ACM Conference on Human Factors in Computing Systems (CHI), 2014.
[PDF] [Poster] [System] [BibTeX]

2013

 
[Joachims/13a] T. Joachims, Learning with Humans in the Loop, ECML Keynote Talk, 2013.
[Slides]
[Raman/etal/13a] K. Raman, T. Joachims, P. Shivaswamy, T. Schnabel, Stable Coactive Learning via Perturbation, International Conference on Machine Learning (ICML), 2013.
[PDF]
[BibTeX]
[Fix/etal/13a] A. Fix, T. Joachims, S. Park, R. Zabih, Structured learning of sum-of-submodular higher order energy functions, International Conference on Computer Vision (ICCV), 2013.
[PDF]
[BibTeX]
[Raman/Joachims/13a] K. Raman, T. Joachims, Learning Socially Optimal Information Systems from Egoistic Users, European Conference on Machine Learning (ECML), 2013.
[PDF]
[BibTeX]
[Raman/etal/13b] K. Raman, A. Swaminathan, J. Gehrke, T. Joachims, Beyond Myopic Inference in Big Data Pipelines, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2013.
[PDF]
[BibTeX]
[Chen/etal/13a] Shuo Chen, Jiexun Xu, T. Joachims, Multi-space Probabilistic Sequence Modeling, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2013.
[PDF]
[BibTeX] [Software]
[Jain/etal/13a] A. Jain, B. Wojcik, T. Joachims, A. Saxena, Learning Trajectory Preferences for Manipulators via Iterative Improvement, Neural Information Processing Systems (NIPS), 2013.
[PDF]
[BibTeX]
[Sipos/Joachims/13a] R. Sipos, T. Joachims, Generating Comparative Summaries from Reviews, short paper, Conference on Information and Knowledge Management (CIKM), 2013.
[PDF]
[BibTeX]
[Moore/etal/13a] J. Moore, Shuo Chen, T. Joachims, D. Turnbull, Taste over Time: the Temporal Dynamics of User Preferences, Conference of the International Society for Music Information Retrieval (ISMIR), 2013.
[PDF]
[BibTeX]

2012

 
[Shivaswamy/Joachims/12a] P. Shivaswamy, T. Joachims, Online Structured Prediction via Coactive Learning, International Conference on Machine Learning (ICML), 2012.
[PDF]
[BibTeX]
[Moore/etal/12a] J. Moore, Shuo Chen, T. Joachims, D. Turnbull, Learning to Embed Songs and Tags for Playlist Prediction, Conference of the International Society for Music Information Retrieval (ISMIR), 2012.
[PDF]
[BibTeX]
[Raman/etal/12b] K. Raman, P. Shivaswamy, T. Joachims, Online Learning to Diversify from Implicit Feedback, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2012.
[PDF]
[BibTeX]
[Chen/etal/12a] Shuo Chen, Joshua Moore, Douglas Turnbull, Thorsten Joachims, Playlist Prediction via Metric Embedding, ACM Conference on Knowledge Discovery and Data Mining (KDD), 2012.
[PDF]
[BibTeX] [Software] [Data] [Online Demo]
[Chapelle/etal/12a] O. Chapelle, T. Joachims, F. Radlinski, Yisong Yue, Large-Scale Validation and Analysis of Interleaved Search Evaluation, ACM Transactions on Information Systems (TOIS), 30(1):6.1-6.41, 2012.
[PDF]
[BibTeX]
[Shivaswamy/Joachims/12b] P. Shivaswamy, T. Joachims, Multi-armed Bandit Problems with History, Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
[PDF]
[BibTeX]
[Anand/etal/12a] A. Anand, H. Koppula, T. Joachims, A. Saxena, Contextually Guided Semantic Labeling and Search for Three-Dimensional Point Clouds, International Journal of Robotics, November, 2012.
[Online] [Software]
[BibTeX]
[Sipos/etal/12a] R. Sipos, P. Shivaswamy, T. Joachims, Large-Margin Learning of Submodular Summarization Models, Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2012.
[PDF]
[BibTeX] [Software]
[Sipos/etal/12b] R. Sipos, A. Swaminathan, P. Shivaswamy, T. Joachims, Temporal Corpus Summarization using Submodular Word Coverage, Conference on Information and Knowledge Management (CIKM), 2012.
[PDF]
[BibTeX]
[Raman/etal/12a] K. Raman, P. Shivaswamy, T. Joachims, Learning to Diversify from Implicit Feedback, WSDM Workshop on Diversity in Document Retrieval, 2012.
[PDF]
[BibTeX]

2011

 
[Shivaswamy/Joachims/11b] P. Shivaswamy, T. Joachims, Online Learning with Preference Feedback, NIPS Workshop on Choice Models and Preference Learning, 2011.
[PDF]
[BibTeX]
[Bennett/etal/11a] P. Bennett and K. El-Arini and T. Joachims and K. Svore, Enriching Information Retrieval, SIGIR Forum, 45(2):60-65, 2011.
[PDF]
[BibTeX]
[Raman/etal/11a] K. Raman, T. Joachims, P. Shivaswamy, Structured Learning of Two-Level Dynamic Rankings, Conference on Information and Knowledge Management (CIKM), 2011.
[PDF]
[BibTeX]
[Koppula/etal/11a] H. Koppula, A. Anand, T. Joachims, A. Saxena, Semantic Labeling of 3D Point Clouds for Indoor Scenes, Conference on Neural Information Processing Systems (NIPS), 2011.
[PDF] [Software]
[BibTeX]
[Yue/Joachims/11a] Yisong Yue, T. Joachims, Beat the Mean Bandit, International Conference on Machine Learning (ICML), 2011.
[PDF]
[BibTeX]
[Yue/etal/11a] Yisong Yue, J. Broder, R. Kleinberg, T. Joachims, The K-armed Dueling Bandits Problem, Journal of Computer and System Sciences, Special Issue of COLT09, to in press.
[Elsevier] [Draft]
[BibTeX]
[Brandt/etal/11a]
Best Paper Nomination
C. Brandt, T. Joachims, Yisong Yue, J. Bank, Dynamic Ranked Retrieval, ACM International Conference on Web Search and Data Mining (WSDM), 2011.
[PDF] [BibTeX] [Software]

2010

 
[Fuernkranz/Joachims/10a] J. Fuernkranz, T. Joachims, Proceedings of the International Conference on Machine Learning (ICML), Haifa, Israel, June 21-24, 2010.
[Online] [Omnipress] [BibTeX]
[Yue/etal/10a] Yisong Yue, Yue Gao, O. Chapelle, Ya Zhang, T. Joachims, Learning more powerful test statistics for click-based retrieval evaluation, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2010.
[PDF]
[BibTeX]
[Xu/etal/10a] Z. Xu, K. Kersting, T. Joachims, Fast Active Exploration for Link-Based Preference Learning using Gaussian Processes, Proceedings of the European Conference on Machine Learning (ECML), 2010.
[PDF] [BibTeX]
[Radlinski/etal/10a] F. Radlinski, M. Kurup, T. Joachims, Evaluating Search Engine Relevance with Click-Based Metrics, in: J. Fuernkranz, E. Huellermeyer, Preference Learning, Springer, 2010. I recommend you read [Radlinski/etal/08b] instead, since Springer charges more than $100 for this book.
[BibTeX]

2009

 
[Joachims/etal/09a] T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, 77(1):27-59, 2009.
[PDF]
[BibTeX] [Software]
[Joachims/etal/09b] T. Joachims, T. Hofmann, Yisong Yue, Chun-Nam Yu, Predicting Structured Objects with Support Vector Machines, Communications of the ACM, Research Highlight, 52(11):97-104, November, 2009 (with Technical Perspective by John Shawe-Taylor).
[Draft] [Online]
[BibTeX]
[Yu/Joachims/09a] Chun-Nam John Yu, T. Joachims, Learning Structural SVMs with Latent Variables, Proceedings of the International Conference on Machine Learning (ICML), 2009.
[PDF]
[BibTeX[Software]
[Yue/Joachims/09a] Yisong Yue, T. Joachims, Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem, Proceedings of the International Conference on Machine Learning (ICML), 2009.
[PDF]
[BibTeX
[Joachims/09a]
Best 10-year Paper Award

 
T. Joachims, Retrospective on Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the International Conference on Machine Learning (ICML), 1999 / 2009.
[Slides]
[ICML99 paper]
[Yue/etal/09a] Yisong Yue, J. Broder, R. Kleinberg, T. Joachims, The K-armed Dueling Bandits Problem, Proceedings of the Conference on Learning Theory (COLT), 2009.
[PDF]
[BibTeX
[Shaparenko/Joachims/09a] B. Shaparenko, T. Joachims, Identifying the Original Contribution of a Document via Language Modeling, poster abstract, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2009.
[PDF] [BibTeX
[Joachims/Yu/09a]
Best Paper Award
T. Joachims, Chun-Nam John Yu, Sparse Kernel SVMs via Cutting-Plane Training, European Conference on Machine Learning (ECML), Machine Learning Journal, Special ECML Issue, 76(2-3):179-193, 2009.
[PDF]
[BibTeX
[Shaparenko/Joachims/09b] B. Shaparenko, T. Joachims, Identifying the Original Contribution of a Document via Language Modeling, Proceedings of the European Conference on Machine Learning (ECML), 2009.
[PDF] [BibTeX

2008

 
[Radlinski/etal/08b] F. Radlinski, M. Kurup, T. Joachims, How Does Clickthrough Data Reflect Retrieval Quality?, Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), 2008.
[PDF]
[BibTeX]
[Yu/etal/08a] Chun-Nam John Yu, T. Joachims, R. Elber, J. Pillardy, Support Vector Training of Protein Alignment Models, Journal of Computational Biology, 15(7): 867-880, September 2008.
[JCB Digital Library]
[BibTeX
[Yu/Joachims/08b] Chun-Nam John Yu, T. Joachims, Training Structural SVMs with Kernels Using Sampled Cuts, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2008.
[PDF]
[BibTeX
[Finley/Joachims/08a] T. Finley, 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, 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, R. Kleinberg, 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]
[Pan/etal/07a] Bing Pan, H. Hembrooke, T. Joachims, L. Lorigo, G. Gay, L. Granka, In Google we Trust: Users' Decisions on Rank, Position, and Relevance, Journal of Computer-Mediated Communication (JCMC), Vol. 12, pp. 801-823, 2007.
[HTML] [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]
Best 10-year Paper Award

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]