Home Page of Thorsten Joachims
Jacob Gould Schurman Professor
Department of Computer Science
Department of Information Science
Associate Dean for Research, Cornell Bowers CIS
eMail: tj@cs.cornell.edu
Phone: (607)255-5593
Fax: (607)255-5196
Address: 418 Gates Hall, Ithaca, NY 14853-7501
Administrative Assistant: Kimberly Budd
Bio
Thorsten Joachims is a Jacob Gould Schurman Professor in the Department of Computer Science and in the Department of Information Science at Cornell University. He is the Associate Dean for Research for Bowers CIS, and he has served as Chair of the Department of Information Science. He is the Director of the Cornell AI Radical Collaboration. He has served as Program Chair of the ICML and KDD conferences, and he is a member of the IMLS Board and the SIGKDD Executive Committee. Thorsten Joachims joined Cornell 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. Working with his students and collaborators, his papers won 11 Best Paper Awards and 4 Test-of-Time Awards. Thorsten Joachims is an ACM Fellow, AAAI Fellow, and member of the SIGIR Academy. [Curriculum Vita]
Research Topics
- Machine Learning Methods and Theory
- Learning from Human Behavioral Data and Implicit Feedback
- Machine Learning for Search Engines, Recommendation, Education, and other Human-Centered Tasks
News, Resources, and Code
- RecSys 2023 Workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES)
- ICML 2023 Workshop on Counterfactuals in Minds and Machines
- KDD 2022 Tutorial on Counterfactual Evaluation and Learning for Interactive Systems
- RecSys 2021 Tutorial on Counterfactual Evaluation and Learning for Recommender Systems
- SIGIR 2016 Tutorial on Counterfactual Evaluation and Learning
- Propensity SVM-Rank - software for learning ranking from partially labeled and biased data
- POEM - software for batch learning from bandit feedback via counterfactual risk minimization
- 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: ArXiv as an Accessible Large-scale Open Research Platform- NSF Project: Designing AI Systems with Steerable Long-Term Dynamics
- NSF Project: Fairness and Control of Exposure in Ranking
- NSF Project: Counterfactual Learning and Evaluation for Interactive Information Systems
WebWatcher - a tour guide for the World Wide Web. - LASER - a retrieval engine for the Web that learns
Teaching
- CS3780/5780 Introduction to Machine Learning, Fall 2024.
- CS6780 Advanced Machine Learning, Fall 2023, Spring 2019, Spring 2015.
- CS7792 Fairness and Dynamics of Learning Systems, Fall 2021.
- CS4780/5780 Introduction to Machine Learning, Spring 2021, (Fall 2020, Fall 2019, Fall 2014, Fall 2013, Fall 2012, Fall 2009, Spring 2008, Spring 2007, Spring 2006, Spring 2005, Spring 2004).
- CS7792 Bias and Fairness in Learning Systems, Spring 2020.
- CS7792 Counterfactual Machine Learning, Fall 2018, (Fall 2016).
- CS6784 Advanced Topics in Machine Learning, Spring 2014, (Fall 2010).
- CS/ENGRD2110 Object-Oriented Programming and Data Structures, Spring 2012, (Spring 2011).
- CS472/473 Foundations of Artificial Intelligence, Fall 2007, (Fall 2005).
- CS778 Topics in Machine Learning: Learning to Predict Structured Objects, Fall 2006.
Ph.D. Students and Postdocs
- Filip Radlinski (Ph.D. 2008)
- Thomas Finley (Ph.D. 2008)
- Benyah Shaparenko (Ph.D. 2009)
- Chun-Nam Yu (Ph.D. 2010)
- Yisong Yue (Ph.D. 2010)
- Ruben Sipos (Ph.D. 2014)
- Pannaga Shivaswamy (Postdoc 2014)
- Joshua Moore (Ph.D. 2015)
- Karthik Raman (Ph.D. 2015)
- Shuo Chen (Ph.D. 2016)
- Adith Swaminathan (Ph.D. 2017)
- Pantelis Analytis (Postdoc 2018)
- Tobias Schnabel (Ph.D. 2018)
- Aman Agarwal (Ph.D. 2020)
- Rahul Kidambi (Postdoc 2020)
- Ashudeep Singh (Ph.D. 2021)
- Yi Su (Ph.D. 2021)
- Luke Lequn Wang (Ph.D. 2023)
- Himank Yadav
- Aaron Tucker
- Joyce Zhou
- Yuta Saito
- Kianté Brantley
- Richa Rastogi
Cornell
Publications
2024 |
|
| [Tucker/etal/24a] |
A. Tucker, K. Brantley, A. Cahall, T. Joachims, Coactive Learning for Large Language Models using Implicit Feedback, International Conference on Machine Learning (ICML), 2024. [PDF] [BibTeX] |
| [Brantley/etal/24a] |
K. Brantley, Zhichong Fang, S. Dean, T. Joachims, Ranking with Long-Term Constraints, ACM Conference on Web Search and Data Mining (WSDM), 2024. [PDF] [BibTeX] |
| [Rastogi/Joachims/24a] |
R. Rastogi, T. Joachims, Fairness in Ranking under Disparate Uncertainty, ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2024. [PDF] [BibTeX] |
| [Lee/etal/24a] |
J. Lee, E. Harvey, Joyce Zhou, N. Garg, T. Joachims, R. Kizilcec, Ending Affirmative Action Harms Diversity Without Improving Academic Merit, ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2024. [PDF] [BibTeX] |
| [Guo/etal/24a] |
Wentao Guo, Andrew Wang, B. Thymes, T. Joachims, Ranking with Slot Constraints, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024. [PDF] [BibTeX] |
| [Buchholz/etal/24a] |
A. Buchholz, B. London, G. Di Benedetto, J. Lichtenberg, Y. Stein, T. Joachims, Counterfactual Ranking Evaluation with Flexible Click Models, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2024. [PDF] [BibTeX] |
| [Alvero/etal/24a] |
A. J. Alvero, Jinsook Lee, A. Regla-Vargas, R. Kizilcec, T. Joachims, A. Antonio, Large Language Models, Social Demography, and Hegemony: Comparing Authorship in Human and Synthetic Text, Journal of Big Data, 11:138, Springer, 2024. [PDF] [BibTeX] |
| [Zhou/etal/24a] |
Joyce Zhou, Yijia Dai, T. Joachims, Language-Based User Profiles for Recommendation, WSDM Workshop on Large Language Models for Individuals, Groups, and Society, 2024. [PDF] [BibTeX] |
| [Kiyohara/etal/24a] |
H. Kiyohara, Y. Saito, Daniel Yiming Cao, T. Joachims, Prompt Optimization with Logged Bandit Data., ICLR Workshop on Navigating and Addressing Data Problems for Foundation Models (DPFM), 2024. [PDF] [BibTeX] |
| [Gao/etal/24a] |
Zhaolin Gao, K. Brantley, T. Joachims, Reviewer2: Optimizing Review Generation Through Prompt Generation, Arxiv Preprint, 2024. [PDF] [BibTeX] |
| [Saito/etal/24a] |
Y. Saito, Yihan Yao, T. Joachims, POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition, Arxiv Preprint, 2024. [PDF] [BibTeX] |
| [Rastogi/etal/24a] |
R. Rastogi, Y. Saito, T. Joachims, MultiScale Policy Learning for Alignment with Long Term Objectives, ICML Workshop on Models of Human Feedback for AI Alignment, 2024. [PDF] [BibTeX] |
2023 |
|
| [Gao/etal/23a] |
Ge Gao, Jonathan Chang, C. Cardie, K. Brantley, T. Joachims, Policy-Gradient Training of Language Models for Ranking, NeurIPS Workshop on Foundation Models for Decision Making, 2023. [PDF] [BibTeX] |
| [Tucker/Joachims/23a] |
A. Tucker, T. Joachims, Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits, ACM Conference on Web Search and Data Mining (WSDM), 2023. [PDF] [BibTeX] |
| [Tucker/etal/23a] |
A. Tucker, C. Biddulph, Claire Wang, T. Joachims, Bandits with Costly Reward Observations, Conference on Uncertainty in Artificial Intelligence (UAI), 2023. [PDF] [BibTeX] |
| [Wang/Joachims/23a] |
Lequn Wang, T. Joachims, Uncertainty Quantification for Fairness in Two-Stage Recommender Systems, ACM Conference on Web Search and Data Mining (WSDM), 2023. [PDF] [BibTeX] |
| [London/etal/23a] |
B. London, Levi Lu, T. Sandler, T. Joachims, Boosted Off-Policy Learning, International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [PDF] [BibTeX] |
| [Jakimov/etal/23a] |
M. Jakimov, A. Buchholz, Y. Stein, T. Joachims, Unbiased Offline Evaluation for Learning to Rank with Business Rules, RecSys Workshop on Causality, Counterfactuals and Sequential Decision-Making, 2023. [PDF] [BibTeX] |
| [Zhou/Joachims/23a] |
Joyce Zhou, T. Joachims, How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making, ACM Conference on Fairness, Accountability and Transparency (FAccT), 2023. [PDF] [BibTeX] |
|
[Lee/etal/23a] Best Paper Award |
Hansol Lee, R. Kizilcec, T. Joachims, Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions, ACM Conference on Learning at Scale (L@S), 2023. [PDF] [BibTeX] |
| [Lee/etal/23b] |
Jinsook Lee, B. Thymes, Joyce Zhou, T. Joachims, R. Kizilcec, Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters, AIED Workshop on Equity, Diversity, and Inclusion in Educational Technology Research and Development, 2023. [PDF] [BibTeX] |
| [Saito/etal/23a] |
Yuta Saito, Qingyang Ren, T. Joachims, Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling, International Conference on Machine Learning (ICML), 2023. [PDF] [BibTeX] |
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, International Conference on Machine Learning (ICML), 2022. [PDF] [BibTeX] |
| [Saito/Joachims/22a] |
Yuta Saito, T. Joachims, Off-Policy Evaluation for Large Action Spaces via Embeddings, International Conference on Machine Learning (ICML), 2022. [PDF] [BibTeX] |
| [Saito/Joachims/22b] |
Yuta Saito, T. Joachims, Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022. [PDF] [BibTeX] |
| [Block/etal/22a] |
A. Block, R. Kidambi, D. Hill, T. Joachims, I. Dhillon, Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2022. [PDF] [BibTeX] |
| [Zhou/Joachims/22a] |
Joyce Zhou, T. Joachims, How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making, IJCAI Workshop on Adverse Impacts and Collateral Effects of Artificial Intelligence Technologies, 2022. [PDF] [BibTeX] |
| [Tucker/Joachims/22a] |
A. Tucker, C. Biddulph, Claire Wang, T. Joachims, Bandits with Costly Reward Observations, NeurIPS Workshop on Machine Learning Safety, 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, Policy-Gradient 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 Two-Sided 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 19-30, 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 Learning-to-Rank, 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, Off-policy 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 Two-Sided 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: Model-Based 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 Real-World Sequential Decision Making, 2019. [PDF] [BibTeX] |
| [Agarwal/etal/19b] |
A. Agarwal, K. Takatsu, I. Zaitsev, T. Joachims, A General Framework for Counterfactual Learning-to-Rank, 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 Context-Dependent Examination-Bias Estimation, ACM Conference on Research and Development in Information Retrieval (SIGIR), 2019. [PDF] [BibTeX] |
| [Yadav/etal/19a] |
H. Yadav, Zhengxiao Du, T. Joachims, Fair Learning-to-Rank 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, Short-Term Satisfaction and Long-Term 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 Learning-to-Rank for Additive Metrics and Deep Models, Pre-Print, 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 Learning-to-Rank, 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 Learning-to-Rank 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 |
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, Large-scale Validation of Counterfactual Learning
Methods: A Test-Bed, NeurIPS 2016 What-If 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 Self-Normalized 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):1731-1755, 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 Web-based 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 Web-based 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] IJCAI-JAIR Best Paper Prize |
P. Shivaswamy, T. Joachims, Coactive Learning, Journal of Artificial Intelligence Research (JAIR), 53:1-40, 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 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 (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, 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/Jo/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/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. 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 (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 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/Jo/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/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,
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/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] |
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 |
|
| [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
Non-Hyperlinked 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] |
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/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] |
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] Test-of-Time 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. |
| [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. |
| [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. |
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. |
| [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. |
| [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. |
|
[Joachims/02b] |
T. Joachims, Evaluating
Retrieval Performance Using Clickthrough Data, Proceedings of the SIGIR
Workshop on Mathematical/Formal Methods in Information Retrieval, 2002. |
|
[Joachims/02c] Test-of-Time Award 2015 |
T. Joachims, Optimizing Search Engines Using Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002. |
|
[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), 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. |
|
[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. |
|
[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. |
|
[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. |
|
[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. |
1999 |
|
|
[Joachims/99a] |
T. Joachims, Making
Large-Scale SVM Learning Practical. In: Advances in Kernel Methods -
Support Vector Learning, B. Schoelkopf, C. Burges, and A. Smola (ed.), MIT
Press, 1999. |
|
[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 2009 |
T. Joachims, Transductive
Inference for Text Classification using Support Vector Machines.
Proceedings of the International Conference on Machine Learning (ICML), 1999. |
|
[Joachims/99d] |
T. Joachims, Aktuelles Schlagwort: Support Vector Machines.
Kuenstliche Intelligenz, Vol. 4, 1999. |
|
[Joachims/99e] |
T. Joachims, Estimating
the Generalization Performance of a SVM Efficiently. LS8-Report
25, Universitaet Dortmund, LS VIII, 1999. |
|
[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. |
|
[Scheffer/Joachims/99a] |
Tobias Scheffer and
Thorsten Joachims, Expected Error Analysis for Model Selection.
Proceedings of the International Conference on Machine Learning (ICML), 1999. |
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. |
|
[Joachims/Mladenic/98a] |
T. Joachims and D.
Mladenic, Browsing-Assistenten, Tour Guides und adaptive WWW-Server. Kuenstliche
Intelligenz, Vol. 3 (28), 1998. |
|
[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. |
|
[Joachims/98c] |
Thorsten Joachims, Making
large-Scale SVM Learning Practical. LS8-Report 24, Universitaet
Dortmund, LS VIII-Report, 1998. |
|
[Scheffer/Joachims/98a] |
Tobias Scheffer and
Thorsten Joachims, Estimating the expected error of empirical minimizers
for model selection. TR-98-9,
TU-Berlin, 1998. |
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. |
|
[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. |
|
[Joachims/97b] |
T. Joachims, Text
Categorization with Support Vector Machines: Learning with Many Relevant
Features. LS8-Report 23, Universitaet Dortmund, LS VIII-Report,
1997. |
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. |
|
[Joachims/96a] |
Joachims, Thorsten, Einsatz eines intelligenten, lernenden Agenten fuer
das World Wide Web. Fachbereich Informatik, Universitaet Dortmund,
Diplomarbeit, 1996. |
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. |
|
[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 GI-Fachgruppe 1.1.3, 1995, Forschungsbericht Nr. 580
der Universitaet Dortmund. |
