| 2025 | CIKM | LangPTune: Optimizing Language-based User Profiles for Recommendation. | Zhaolin Gao, Joyce Zhou, Yijia Dai, Thorsten Joachims |
| 2025 | ICLR | POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition. | Yuta Saito, Jihan Yao, Thorsten Joachims |
| 2025 | RecSys | An Off-Policy Learning Approach for Steering Sentence Generation towards Personalization. | Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims |
| 2025 | RecSys | CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Harrie Oosterhuis, Olivier Jeunen, Yuta Saito, Yixin Wang, Flavian Vasile, Thorsten Joachims |
| 2024 | ICML | Coactive Learning for Large Language Models using Implicit User Feedback. | Aaron David Tucker, Kiant Brantley, Adam Cahall, Thorsten Joachims |
| 2024 | KDD | Ranking with Slot Constraints. | Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims |
| 2024 | SIGIR | Counterfactual Ranking Evaluation with Flexible Click Models. | Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims |
| 2024 | WSDM | Ranking with Long-Term Constraints. | Kiant Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims |
| 2023 | AISTATS | Boosted Off-Policy Learning. | Ben London, Levi Lu, Ted Sandler, Thorsten Joachims |
| 2023 | ICML | Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. | Yuta Saito, Qingyang Ren, Thorsten Joachims |
| 2023 | RecSys | CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang |
| 2023 | RecSys | Localify.org: Locally-focus Music Artist and Event Recommendation. | Douglas Turnbull, April Trainor, Douglas R. Turnbull, Elizabeth Richards, Kieran Bentley, Victoria Conrad, Paul Gagliano, Cassandra Raineault, Thorsten Joachims |
| 2023 | UAI | Bandits with costly reward observations. | Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims |
| 2023 | WSDM | Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. | Aaron David Tucker, Thorsten Joachims |
| 2023 | WSDM | Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. | Lequn Wang, Thorsten Joachims |
| 2022 | ICML | Off-Policy Evaluation for Large Action Spaces via Embeddings. | Yuta Saito, Thorsten Joachims |
| 2022 | ICML | Improving Screening Processes via Calibrated Subset Selection. | Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez |
| 2022 | KDD | Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. | Yuta Saito, Thorsten Joachims |
| 2022 | KDD | Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances. | Yuta Saito, Thorsten Joachims |
| 2022 | RecSys | CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile |
| 2022 | WWW | Optimizing Rankings for Recommendation in Matching Markets. | Yi Su, Magd Bayoumi, Thorsten Joachims |
| 2022 | SIGIR | Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. | Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon |
| 2021 | ICML | Fairness of Exposure in Stochastic Bandits. | Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims |
| 2021 | IJCAI | Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract). | Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims |
| 2021 | ICTIR | Fairness and Control of Exposure in Two-sided Markets. | Thorsten Joachims |
| 2021 | ICTIR | User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. | Lequn Wang, Thorsten Joachims |
| 2021 | RecSys | Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances. | Yuta Saito, Thorsten Joachims |
| 2021 | SIGIR | Policy-Gradient Training of Fair and Unbiased Ranking Functions. | Himank Yadav, Zhengxiao Du, Thorsten Joachims |
| 2020 | KDD | Off-policy Bandits with Deficient Support. | Noveen Sachdeva, Yi Su, Thorsten Joachims |
| 2020 | RecSys | REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. | Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan |
| 2020 | SIGIR | Controlling Fairness and Bias in Dynamic Learning-to-Rank. | Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims |
| 2020 | SIGIR | The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. | Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims |
| 2019 | ICML | CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. | Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims |
| 2019 | RecSys | REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. | Thorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile |
| 2019 | SIGIR | A General Framework for Counterfactual Learning-to-Rank. | Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims |
| 2019 | SIGIR | Intervention Harvesting for Context-Dependent Examination-Bias Estimation. | Zhichong Fang, Aman Agarwal, Thorsten Joachims |
| 2019 | WSDM | Estimating Position Bias without Intrusive Interventions. | Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims |
| 2019 | WSDM | Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. | Tobias Schnabel, Paul N. Bennett, Thorsten Joachims |
| 2018 | ICLR | Deep Learning with Logged Bandit Feedback. | Thorsten Joachims, Adith Swaminathan, Maarten de Rijke |
| 2018 | IJCAI | Unbiased Learning-to-Rank with Biased Feedback. | Thorsten Joachims, Adith Swaminathan, Tobias Schnabel |
| 2018 | KDD | Fairness of Exposure in Rankings. | Ashudeep Singh, Thorsten Joachims |
| 2018 | RecSys | Deep Learning from Logged Interventions. | Thorsten Joachims |
| 2018 | RecSys | REVEAL 2018: offline evaluation for recommender systems. | Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile |
| 2018 | WSDM | Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. | Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims |
| 2017 | ICWSM | Ranking with Social Cues: Integrating Online Review Scores and Popularity Information. | Pantelis P. Analytis, Alexia Delfino, Juliane E. Kmmer, Mehdi Moussad, Thorsten Joachims |
| 2017 | KDD | Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. | Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims |
| 2017 | WSDM | Unbiased Learning-to-Rank with Biased Feedback. | Thorsten Joachims, Adith Swaminathan, Tobias Schnabel |
| 2016 | ICML | Recommendations as Treatments: Debiasing Learning and Evaluation. | Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims |
| 2016 | ICTIR | Unbiased Comparative Evaluation of Ranking Functions. | Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims |
| 2016 | KDD | Predicting Matchups and Preferences in Context. | Shuo Chen, Thorsten Joachims |
| 2016 | KDD | Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. | Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims |
| 2016 | WWW | Using Shortlists to Support Decision Making and Improve Recommender System Performance. | Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims |
| 2016 | SIGIR | Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. | Thorsten Joachims, Adith Swaminathan |
| 2016 | WSDM | Modeling Intransitivity in Matchup and Comparison Data. | Shuo Chen, Thorsten Joachims |
| 2015 | EMNLP | Evaluation methods for unsupervised word embeddings. | Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims |
| 2015 | ICML | Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. | Adith Swaminathan, Thorsten Joachims |
| 2015 | WWW | Unbiased Ranking Evaluation on a Budget. | Tobias Schnabel, Adith Swaminathan, Thorsten Joachims |
| 2015 | WWW | Counterfactual Risk Minimization. | Adith Swaminathan, Thorsten Joachims |
| 2015 | WSDM | Learning from User Interactions. | Thorsten Joachims |
| 2014 | CHI | Using personalized radio to enhance local music discovery. | Douglas R. Turnbull, Justin A. Zupnick, Kristofer B. Stensland, Andrew R. Horwitz, Alexander J. Wolf, Alexander E. Spirgel, Stephen P. Meyerhofer, Thorsten Joachims |
| 2014 | EMNLP | Invited Talk: Learning from Rational Behavior. | Thorsten Joachims |
| 2014 | ICML | Reducing Dueling Bandits to Cardinal Bandits. | Nir Ailon, Zohar Shay Karnin, Thorsten Joachims |
| 2014 | KDD | Methods for ordinal peer grading. | Karthik Raman, Thorsten Joachims |
| 2014 | WWW | Was this review helpful to you?: it depends! context and voting patterns in online content. | Ruben Sipos, Arpita Ghosh, Thorsten Joachims |
| 2013 | CIKM | Generating comparative summaries from reviews. | Ruben Sipos, Thorsten Joachims |
| 2013 | ICCV | Structured Learning of Sum-of-Submodular Higher Order Energy Functions. | Alexander Fix, Thorsten Joachims, Sung Min Park, Ramin Zabih |
| 2013 | ICML | Stable Coactive Learning via Perturbation. | Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel |
| 2013 | KDD | Multi-space probabilistic sequence modeling. | Shuo Chen, Jiexun Xu, Thorsten Joachims |
| 2013 | KDD | Beyond myopic inference in big data pipelines. | Karthik Raman, Adith Swaminathan, Johannes Gehrke, Thorsten Joachims |
| 2012 | CIKM | Temporal corpus summarization using submodular word coverage. | Ruben Sipos, Adith Swaminathan, Pannaga Shivaswamy, Thorsten Joachims |
| 2012 | EACL | Large-Margin Learning of Submodular Summarization Models. | Ruben Sipos, Pannaga Shivaswamy, Thorsten Joachims |
| 2012 | ICML | Online Structured Prediction via Coactive Learning. | Pannaga Shivaswamy, Thorsten Joachims |
| 2012 | KDD | Playlist prediction via metric embedding. | Shuo Chen, Joshua L. Moore, Douglas R. Turnbull, Thorsten Joachims |
| 2012 | KDD | Online learning to diversify from implicit feedback. | Karthik Raman, Pannaga Shivaswamy, Thorsten Joachims |
| 2011 | CIKM | Structured learning of two-level dynamic rankings. | Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy |
| 2011 | ECIR | The Value of User Feedback. | Thorsten Joachims |
| 2011 | ICML | Beat the Mean Bandit. | Yisong Yue, Thorsten Joachims |
| 2011 | WSDM | Dynamic ranked retrieval. | Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank |
| 2010 | SIGIR | Learning more powerful test statistics for click-based retrieval evaluation. | Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims |
| 2009 | COLT | The K-armed Dueling Bandits Problem. | Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims |
| 2009 | ICML | Interactively optimizing information retrieval systems as a dueling bandits problem. | Yisong Yue, Thorsten Joachims |
| 2009 | ICML | Learning structural SVMs with latent variables. | Chun-Nam John Yu, Thorsten Joachims |
| 2009 | SIGIR | Identifying the original contribution of a document via language modeling. | Benyah Shaparenko, Thorsten Joachims |
| 2008 | CIKM | How does clickthrough data reflect retrieval quality? | Filip Radlinski, Madhu Kurup, Thorsten Joachims |
| 2008 | ICML | Training structural SVMs when exact inference is intractable. | Thomas Finley, Thorsten Joachims |
| 2008 | ICML | Learning diverse rankings with multi-armed bandits. | Filip Radlinski, Robert Kleinberg, Thorsten Joachims |
| 2008 | ICML | Predicting diverse subsets using structural SVMs. | Yisong Yue, Thorsten Joachims |
| 2008 | KDD | Training structural svms with kernels using sampled cuts. | Chun-Nam John Yu, Thorsten Joachims |
| 2007 | KDD | Active exploration for learning rankings from clickthrough data. | Filip Radlinski, Thorsten Joachims |
| 2007 | KDD | Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases. | Benyah Shaparenko, Thorsten Joachims |
| 2007 | RECOMB | Support Vector Training of Protein Alignment Models. | Chun-Nam John Yu, Thorsten Joachims, Ron Elber, Jaroslaw Pillardy |
| 2007 | SIGIR | A support vector method for optimizing average precision. | Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims |
| 2006 | AAAI | Minimally Invasive Randomization fro Collecting Unbiased Preferences from Clickthrough Logs. | Filip Radlinski, Thorsten Joachims |
| 2006 | KDD | Training linear SVMs in linear time. | Thorsten Joachims |
| 2006 | SSPR | Structured Output Prediction with Support Vector Machines. | Thorsten Joachims |
| 2005 | ICML | Supervised clustering with support vector machines. | Thomas Finley, Thorsten Joachims |
| 2005 | ICML | A support vector method for multivariate performance measures. | Thorsten Joachims |
| 2005 | ICML | Error bounds for correlation clustering. | Thorsten Joachims, John E. Hopcroft |
| 2005 | KDD | Query chains: learning to rank from implicit feedback. | Filip Radlinski, Thorsten Joachims |
| 2005 | SIGIR | Accurately interpreting clickthrough data as implicit feedback. | Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay |
| 2005 | UAI | Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation. | Carmel Domshlak, Thorsten Joachims |
| 2004 | ICML | Support vector machine learning for interdependent and structured output spaces. | Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun |
| 2004 | SIGIR | Eye-tracking analysis of user behavior in WWW search. | Laura A. Granka, Thorsten Joachims, Geri Gay |
| 2003 | ICML | Transductive Learning via Spectral Graph Partitioning. | Thorsten Joachims |
| 2002 | KDD | Optimizing search engines using clickthrough data. | Thorsten Joachims |
| 2001 | ICML | Composite Kernels for Hypertext Categorisation. | Thorsten Joachims, Nello Cristianini, John Shawe-Taylor |
| 2001 | SIGIR | A Statistical Learning Model of Text Classification for Support Vector Machines. | Thorsten Joachims |
| 2000 | ICML | Estimating the Generalization Performance of an SVM Efficiently. | Thorsten Joachims |
| 2000 | ICML | Detecting Concept Drift with Support Vector Machines. | Ralf Klinkenberg, Thorsten Joachims |
| 1999 | ICML | Transductive Inference for Text Classification using Support Vector Machines. | Thorsten Joachims |
| 1999 | ICML | Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. | Katharina Morik, Peter Brockhausen, Thorsten Joachims |
| 1999 | ICML | Expected Error Analysis for Model Selection. | Tobias Scheffer, Thorsten Joachims |
| 1998 | AAAI | Estimating the Expected Error of Empirical Minimizers for Model Selection. | Tobias Scheffer, Thorsten Joachims |
| 1997 | ICML | A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. | Thorsten Joachims |
| 1997 | IJCAI | Web Watcher: A Tour Guide for the World Wide Web. | Thorsten Joachims, Dayne Freitag, Tom M. Mitchell |