
Publications
2022 

[Su/etal/22a] 
Yi Su, M. Bayoumi, T. Joachims, Optimizing Rankings for Recommendation in Matching Markets, ACM Web Conference (WWW), 2022.
[PDF] [BibTeX] 
[Wang/etal/22a] 
Lequn Wang, T. Joachims, Manuel Gomez Rodriguez, Improving Screening Processes via Calibrated Subset Selection, ArXiv Preprint, 2022.
[PDF] [BibTeX] 
[Tucker/Joachims/22a] 
A. Tucker, T. Joachims, VarianceOptimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits, ArXiv Preprint, 2022.
[PDF] [BibTeX] 
2021 

[Wang/etal/21a] 
Lequn Wang, Yiwei Bai, Wen Sun, T. Joachims, Fairness of Exposure in Stochastic Bandits, International Conference on Machine Learning (ICML), 2021.
[PDF] [BibTeX] 
[Yadav/etal/21a] 
H. Yadav, Zhengxiao Du, T. Joachims, PolicyGradient Training of Fair and Unbiased Ranking Functions, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2021.
[PDF] [BibTeX] 
[Wang/Joachims/21a] 
Lequn Wang, T. Joachims, User Fairness, Item Fairness and Diversity for Rankings in TwoSided Markets, ACM International Conference on the Theory of Information Retrieval (ICTIR), 2021.
[PDF] [BibTeX] 
[Joachims/etal/21a] 
T. Joachims, B. London, Yi Su, A. Swaminathan, Lequn Wang, Recommendations as Treatments, AAAI AI Magazine, vol 42, number 3, pages 1930, Fall 2021.
[PDF] [BibTeX] 
[Singh/etal/21a] 
A. Singh, D. Kempe, T. Joachims, Fairness in Ranking under Uncertainty, Neural Information Processing Systems (NeurIPS), 2021.
[PDF] [Arxiv] [BibTeX] 
2020 

[Morik/etal/20a]
Best Paper Award 
M. Morik, A. Singh, J. Hong, T. Joachims, Controlling Fairness and Bias in Dynamic LearningtoRank, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2020.
[PDF] [BibTeX] 
[Schnabel/etal/20a] 
T. Schnabel, S. Amershi, P. Bennett, P. Bailey, T. Joachims, The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2020.
[PDF] [BibTeX] 
[Sachdeva/etal/20a] 
N. Sachdeva, Yi Su, T. Joachims, Offpolicy Bandits with Deficient Support, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020.
[PDF] [Software] [BibTeX] 
[Su/Joachims/20a] 
Yi Su, T. Joachims, Rankings for TwoSided Market Platforms, NeurIPS Workshop on Consequential Decisions in Dynamic Environments, 2020.
[PDF] [BibTeX] 
[Kidambi/etal/20a] 
R. Kidambi, A. Rajeswaran, P. Netrapalli, T. Joachims, MOReL: ModelBased Offline Reinforcement Learning, Conference on Neural Information Processing Systems (NeurIPS), 2020.
[PDF] [BibTeX] 
2019 

[Agarwal/etal/19a] 
A. Agarwal, I. Zaitsev, Xuanhui Wang, Cheng Li, M. Najork, T. Joachims, Estimating Position Bias Without Intrusive Interventions, International Conference on Web Search and Data Mining (WSDM), 2019.
[PDF] [BibTeX] 
[Schnabel/etal/19a] 
T. Schnabel, P. Bennett, T. Joachims, Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory, International Conference on Web Search and Data Mining (WSDM), 2019.
[PDF] [BibTeX] 
[Su/etal/19a] 
Yi Su, Lequn Wang, M. Santacatterina, T. Joachims, CAB: Continuous Adaptive Blending for Policy Evaluation and Learning, International Conference on Machine Learning (ICML), 2019.
[PDF] [BibTeX] 
[Wang/etal/19a] 
Lequn Wang, Yiwei Bai, A. Bhalla, T. Joachims, Batch Learning from Bandit Feedback through Bias Corrected Reward Imputation, ICML Workshop on RealWorld Sequential Decision Making, 2019.
[PDF] [BibTeX] 
[Agarwal/etal/19b] 
A. Agarwal, K. Takatsu, I. Zaitsev, T. Joachims, A General Framework for Counterfactual LearningtoRank, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2019.
[PDF] [BibTeX] 
[Fang/etal/19a] 
Zhichong Fang, A. Agarwal, T. Joachims, Intervention Harvesting for ContextDependent ExaminationBias Estimation, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2019.
[PDF] [BibTeX] 
[Yadav/etal/19a] 
H. Yadav, Zhengxiao Du, T. Joachims, Fair LearningtoRank from Implicit Feedback, Arxiv, 2019.
[PDF] [BibTeX] 
[Singh/Joachims/19a] 
A. Singh, T. Joachims, Policy Learning for Fairness in Ranking, Neural Information Processing Systems (NeurIPS), 2019.
[PDF] [BibTeX] 
2018 

[Joachims/etal/18a] 
T. Joachims, A. Swaminathan, M. de Rijke, Deep Learning with Logged Bandit Feedback, International Conference on Learning Representations (ICLR), 2018.
[PDF] [Software] [BibTeX] 
[Schnabel/etal/18a] 
T. Schnabel, P. Bennett, S. Dumais, T. Joachims, ShortTerm Satisfaction and LongTerm Coverage: Understanding How Users Tolerate Algorithmic Exploration, International Conference on Web Search and Data Mining (WSDM), 2018.
[PDF] [BibTeX] 
[Singh/Joachims/18a] 
A. Singh, T. Joachims, Fairness of Exposure in Rankings, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2018.
[PDF] [BibTeX] 
[Agarwal/etal/18b] 
A. Agarwal, I. Zaitsev, T. Joachims, Counterfactual LearningtoRank for Additive Metrics and Deep Models, PrePrint, January 2018.
[PDF] [BibTeX] 
[Su/etal/18a] 
Yi Su, A. Agarwal, T. Joachims, Learning from Logged Bandit Feedback of Multiple Loggers, ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML), 2018.
[PDF] [BibTeX] 
[Agarwal/etal/18c] 
A. Agarwal, I. Zaitsev, T. Joachims, Consistent Position Bias Estimation without Online Interventions for LearningtoRank, ICML Workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action (CausalML), 2018.
[PDF] [BibTeX] 
2017 

[Joachims/etal/17a]
Best Paper Award 
T. Joachims, A. Swaminathan, T. Schnabel, Unbiased LearningtoRank with Biased Feedback, International Conference on Web Search and Data Mining (WSDM), 2017.
[PDF] [Software] [BibTeX] 
[Agarwal/etal/17a] 
A. Agarwal, S. Basu, T. Schnabel, T. Joachims, Effective Evaluation using Logged Bandit Feedback from Multiple Loggers, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2017.
[PDF] [BibTeX] 
[Analytis/etal/17a] 
P. Analytis, A. Delfino, J. Kammer, M. Moussaid, and T. Joachims, Ranking with social cues: Integrating average review scores with popularity information, Short Paper, International Conference in Web and Social Media (ICWSM), 2017.
[PDF] [BibTeX] 
[Singh/Joachims/17a] 
P. Singh and T. Joachims, Learning Item Embeddings using Biased Feedback, NeurIPS Workshop on Causal Inference and Machine Learning for Intelligent Decision Making, 2017.
[PDF] [BibTeX] 
2016 

[Jo/Swaminathan/16] 
T. Joachims, A. Swaminathan, Tutorial on Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2016.
[PDF] [Video] [Slides] [BibTeX] 
[Schnabel/etal/16b] 
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, T. Joachims, Recommendations as Treatments: Debiasing Learning and Evaluation, International Conference on Machine Learning (ICML), 2016.
[PDF] [BibTeX] 
[Schnabel/etal/16c]
Best Presentation Award 
T. Schnabel, A. Swaminathan, P. Frazier, T. Joachims, Unbiased Comparative Evaluation of Ranking Functions, International Conference on the Theory of Information Retrieval, 2016.
[PDF] [BibTeX] 
[Schnabel/etal/16a] 
T. Schnabel, P. Bennett, S. Dumais, T. Joachims, Using Shortlists to Support Decision Making and Improve Recommender System Performance, World Wide Web Conference (WWW), 2016.
[PDF] [BibTeX] 
[Chen/Joachims/16a] 
Shuo Chen, T. Joachims, Modeling Intransitivity in Matchup and Comparison Data, ACM Conference on Web Search and Data Mining (WSDM), 2016.
[PDF] [BibTeX] 
[Chen/Joachims/16b]
Best Student Paper Award RunnerUp 
Shuo Chen, T. Joachims, Predicting Matchups and Preferences in Context, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
[PDF] [BibTeX] 
[Lefortier/etal/16a] 
D. Lefortier, A. Swaminathan, Xiaotao Gu, T. Joachims, M. de Rijke, Largescale Validation of Counterfactual Learning
Methods: A TestBed, NeurIPS 2016 WhatIf Workshop, 2016.
[PDF] [Data and Software] [BibTeX] 
[Reddy/etal/16a] 
Siddharth Reddy, Igor Labutov, T. Joachims, Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation, Work in Progress, ACM Conference on Learning at Scale (L@S), 2016.
[PDF] [Extended Version] [BibTeX] 
[Reddy/etal/16c] 
Siddharth Reddy, Igor Labutov, S. Banerjee, T. Joachims, Unbounded Human Learning: Optimal Scheduling for Spaced Repetition, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
[PDF] [BibTeX] 
2015 

[Joachims/15a] 
T. Joachims, Learning from User Interactions through Interventions, WSDM Keynote Talk, 2015.
[Slides] 
[Joachims/15b] 
T. Joachims, Learning from Rational Behavior, Computing in the 21st Century, Keynote Talk, 2015.
[Video] 
[Swaminathan/Jo/15d] 
A. Swaminathan, T. Joachims, The SelfNormalized Estimator for Counterfactual Learning, Neural Information Processing Systems (NeurIPS), 2015.
[PDF] [Software] [BibTeX]

[Swaminathan/Jo/15c] 
A. Swaminathan, T. Joachims, Batch Learning from Logged Bandit Feedback through
Counterfactual Risk Minimization, Journal of Machine Learning Research (JMLR), Special Issue in Memory of Alexey Chervonenkis, 16(1):17311755, 2015.
[PDF] [Software] [BibTeX]

[Swaminathan/Jo/15b] 
A. Swaminathan, T. Joachims, Counterfactual Risk Minimization: Learning from Logged Bandit Feedback, International Conference on Machine Learning (ICML), 2015.
[PDF] [Software] [BibTeX]

[Schnabel/etal/15b] 
T. Schnabel, I. Labutov, D. Mimno, T. Joachims, Evaluation Methods for Unsupervised Word Embeddings, Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
[PDF] [Data] [BibTeX]

[Joachims/Raman/15a] 
T. Joachims, K. Raman, Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015, Festschrift for Katharina Morik, Springer, 2016.
[PDF] [BibTeX]

[Swaminathan/Jo/15a] 
A. Swaminathan, T. Joachims, Counterfactual Risk Minimization: Learning from Logged Bandit Feedback, WWW Workshop on Offline and Online Evaluation of Webbased Services, 2015.
[PDF] [PDF (extended version)] [BibTeX]

[Schnabel/etal/15a] 
T. Schnabel, A. Swaminathan, T. Joachims, Unbiased Ranking Evaluation on a Budget, WWW Workshop on Offline and Online Evaluation of Webbased Services, 2015.
[PDF] [BibTeX]

[Raman/Joachims/15a] 
K. Raman, T. Joachims, Bayesian Ordinal Peer Grading, ACM Conference on Learning at Scale (L@S), 2015.
[PDF] [Online Peergrading Service] [Software] [BibTeX]

[Shivaswamy/Jo/15a]
IJCAIJAIR Best Paper Prize Honorable Mention 
P. Shivaswamy, T. Joachims, Coactive Learning, Journal of Artificial Intelligence Research (JAIR), 53:140, 2015.
[PDF] [BibTeX]

[Joachims/etal/15a] 
T. Joachims, G. Webb, D. Margineantu, G. Williams, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia, 2015.
[Online] [BibTeX]

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]

[Moore/etal/14a]
Best Student Paper Award 
J. Moore, T. Joachims, D. TurnbullTaste Space Versus the World: an Embedding Analysis of Listening Habits and Geography, Conference of the International Society for Music Information Retrieval (ISMIR), 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 sumofsubmodular 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, Multispace 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 (NeurIPS), 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/Jo/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, LargeScale Validation and Analysis of Interleaved Search
Evaluation, ACM Transactions on Information Systems (TOIS), 30(1):6.16.41, 2012.
[PDF]
[BibTeX] 
[Shivaswamy/Jo/12b] 
P. Shivaswamy, T. Joachims,
Multiarmed 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 ThreeDimensional Point Clouds,
International Journal of Robotics, November, 2012.
[Online] [Software]
[BibTeX] 
[Sipos/etal/12a] 
R. Sipos, P. Shivaswamy, T. Joachims,
LargeMargin 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/Jo/11b] 
P. Shivaswamy,
T. Joachims, Online Learning with Preference Feedback, NeurIPS Workshop
on Choice Models and Preference Learning, 2011.
[PDF]
[BibTeX] 
[Bennett/etal/11a] 
P. Bennett and K. ElArini and T. Joachims and
K. Svore, Enriching Information Retrieval, SIGIR Forum, 45(2):6065, 2011.
[PDF]
[BibTeX] 
[Raman/etal/11a] 
K. Raman,
T. Joachims,
P. Shivaswamy,
Structured Learning of TwoLevel 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 (NeurIPS), 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 Karmed 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/Jo/10a] 
J. Fuernkranz, T. Joachims, Proceedings of
the International Conference on Machine Learning (ICML), Haifa, Israel,
June 2124, 2010.
[Online]
[Omnipress]
[BibTeX]

[Yue/etal/10a] 
Yisong Yue, Yue Gao, O. Chapelle, Ya Zhang, T.
Joachims,
Learning more powerful test statistics for clickbased 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 LinkBased
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 ClickBased 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, ChunNam Yu,
CuttingPlane Training of Structural SVMs, Machine Learning, 77(1):2759,
2009.
[PDF]
[BibTeX]
[Software] 
[Joachims/etal/09b] 
T. Joachims, T. Hofmann, Yisong Yue, ChunNam Yu,
Predicting Structured Objects with Support Vector Machines,
Communications of the ACM, Research Highlight, 52(11):97104, November, 2009
(with Technical Perspective by John ShaweTaylor).
[Draft]
[Online]
[BibTeX] 
[Yu/Joachims/09a] 
ChunNam 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 10year 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 Karmed
Dueling Bandits Problem, Proceedings of the Conference on Learning
Theory (COLT), 2009.
[PDF]
[BibTeX] 
[Shaparenko/Jo/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,
ChunNam John
Yu, Sparse Kernel SVMs via CuttingPlane Training,
European Conference on Machine Learning (ECML), Machine Learning Journal,
Special ECML Issue, 76(23):179193, 2009.
[PDF]
[BibTeX] 
[Shaparenko/Jo/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] 
ChunNam John Yu, T. Joachims, R. Elber, J. Pillardy,
Support Vector Training of Protein Alignment Models, Journal of
Computational Biology, 15(7): 867880,
September 2008.
[JCB Digital Library]
[BibTeX] 
[Yu/Joachims/08b] 
ChunNam 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 MultiArmed Bandits,
Proceedings of the
International Conference on Machine Learning (ICML), 2008.
[PDF]
[BibTeX] 
2007


[Jo/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/Jo/07a] 
B. Shaparenko,
T. Joachims, Information Genealogy: Uncovering the Flow of Ideas in
NonHyperlinked Document Databases,
Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM,
2007.
[PDF]
[BibTeX] 
[Radlinski/Jo/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] 
ChunNam 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 NonParametric Reasoning over User
Preferences, User Modeling and UserAdapted Interaction (UMUAI), Vol. 17,
No. 12, pp. 4169, 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 ComputerMediated
Communication (JCMC), Vol. 12,
pp. 801823,
2007.
[HTML]
[BibTeX] 
2006


[Joachims/06a]
Best Research Paper Award TestofTime Award 2017 
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/Jo/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] 
ChunNam 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
(TDM05), 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]
TestofTime Award 2016 
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/Jo/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/Jo/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 NonParametric
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
MaximumMargin
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):14531484, 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, EyeTracking Analysis of User Behavior in WWWSearch, 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):95108,
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 52365240, 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 (NeurIPS), 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 MaximumMargin 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. 7996,
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]
TestofTime Award 2015 
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 MaximumMargin Approach to Learning Text Classifiers, Ausgezeichnete Informatikdissertationen 2001, D. Wagner et al. (Hrsg.), GIEdition  Lecture Notes in Informatics (LNI), Koellen 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. Goerz, C. Rollinger, J.
Schneeberger, Handbuch der kuenstlichen 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. ShaweTaylor, 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
KnowledgeBased Approach to EvidenceBased 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/Jo/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
LargeScale SVM Learning Practical. In: Advances in Kernel Methods 
Support Vector Learning, B. Schoelkopf, 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 10year Paper Award 2009

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.
Kuenstliche Intelligenz, Vol. 4, 1999.
[BibTeX]

[Joachims/99e]

T. Joachims, Estimating
the Generalization Performance of a SVM Efficiently. LS8Report
25, Universitaet Dortmund, LS VIII, 1999.
[Postscript (gz)]
[BibTeX] [Software]

[Morik/etal/99a]

K. Morik, P. Brockhausen,
and T. Joachims, Combining statistical learning with a knowledgebased
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, BrowsingAssistenten, Tour Guides und adaptive WWWServer. Kuenstliche
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
largeScale SVM Learning Practical. LS8Report 24, Universitaet
Dortmund, LS VIIIReport, 1998.
[Postscript (gz)] [PDF]
[BibTeX]
[Software]

[Scheffer/Joachims/98a]

Tobias Scheffer and
Thorsten Joachims, Estimating the expected error of empirical minimizers
for model selection. TR989,
TUBerlin, 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. LS8Report 23, Universitaet Dortmund, LS VIIIReport,
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 fuer
das World Wide Web. Fachbereich Informatik, Universitaet 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. Beitraege zum 7. Fachgruppentreffen
MASCHINELLES LERNEN der GIFachgruppe 1.1.3, 1995, Forschungsbericht Nr. 580
der Universitaet Dortmund.
[Postscript (gz)] [PDF]
[BibTeX]

