A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. - Thorsten Joachims (ICML, 1997)

Web Watcher: A Tour Guide for the World Wide Web. - Thorsten Joachims; Dayne Freitag; Tom M. Mitchell (IJCAI (1), 1997)

Estimating the Expected Error of Empirical Minimizers for Model Selection. - Tobias Scheffer; Thorsten Joachims (AAAI/IAAI, 1998)

Text Categorization with Support Vector Machines: Learning with Many Relevant Features. - Thorsten Joachims (ECML, 1998)

Browsing-Assistenten, Tour Guides und adaptive WWW-Server. - Thorsten Joachims; Dunja Mladenic (Künstliche Intell., 1998)

Transductive Inference for Text Classification using Support Vector Machines. - Thorsten Joachims (ICML, 1999)

Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. - Katharina Morik; Peter Brockhausen; Thorsten Joachims (ICML, 1999)

Expected Error Analysis for Model Selection. - Tobias Scheffer; Thorsten Joachims (ICML, 1999)

Support Vector Machines (Aktuelles Schlagwort). - Thorsten Joachims (Künstliche Intell., 1999)

Estimating the Generalization Performance of an SVM Efficiently. - Thorsten Joachims (ICML, 2000)

Detecting Concept Drift with Support Vector Machines. - Ralf Klinkenberg; Thorsten Joachims (ICML, 2000)

Erratum to "Knowledge discovery and knowledge validation in intensive care". - Katharina Morik; Michael Imhoff; Peter Brockhausen; Thorsten Joachims; Ursula Gather (Artif. Intell. Medicine, 2000)

Knowledge discovery and knowledge validation in intensive care. - Katharina Morik; Michael Imhoff; Peter Brockhausen; Thorsten Joachims; Ursula Gather (Artif. Intell. Medicine, 2000)

The maximum-margin approach to learning text classifiers: methods theory, and algorithms. - Thorsten Joachims (Ausgezeichnete Informatikdissertationen, 2001)

Composite Kernels for Hypertext Categorisation. - Thorsten Joachims; Nello Cristianini; John Shawe-Taylor (ICML, 2001)

A Statistical Learning Model of Text Classification for Support Vector Machines. - Thorsten Joachims (SIGIR, 2001)

The Maximum-Margin Approach to Learning Text Classifiers. - Thorsten Joachims (Künstliche Intell., 2001)

Learning to classify text using support vector machines - methods, theory and algorithms. - Thorsten Joachims (Kluwer, 2002)

The enigmatics of affect. - Phoebe Sengers; Rainer Liesendahi; Werner Magar; Christoph Seibert; Boris Müller; Thorsten Joachims; Weidong Geng; Pia Mårtensson; Kristina Höök (Symposium on Designing Interactive Systems, 2002)

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis. - Andreas Christmann; Paul Fischer; Thorsten Joachims (COMPSTAT, 2002)

Optimizing search engines using clickthrough data. - Thorsten Joachims (KDD, 2002)

Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications. - Andreas Christmann; Paul Fischer; Thorsten Joachims (Comput. Stat., 2002)

Guest Editors' Introduction to the Special Issue on Automated Text Categorization. - Thorsten Joachims; Fabrizio Sebastiani 001 (J. Intell. Inf. Syst., 2002)

Text-Mining - Serviceteil. - Thorsten Joachims; Edda Leopold (Künstliche Intell., 2002)

Maschinelles Lernen und Data Mining. - Stefan Wrobel; Katharina Morik; Thorsten Joachims (Handbuch der Künstlichen Intelligenz, 2003)

Evaluating Retrieval Performance Using Clickthrough Data. - Thorsten Joachims (Text Mining, 2003)

Transductive Learning via Spectral Graph Partitioning. - Thorsten Joachims (ICML, 2003)

Learning a Distance Metric from Relative Comparisons. - Matthew Schultz; Thorsten Joachims (NIPS, 2003)

Mapping Subsets of Scholarly Information - Paul Ginsparg; Paul Houle; Thorsten Joachims; Jae-Hoon Sul (CoRR, 2003)

SIGIR 2003 workshop report: implicit measures of user interests and preferences. - Susan T. Dumais; Thorsten Joachims; Krishna Bharat; Andreas S. Weigend (SIGIR Forum, 2003)

Support vector machine learning for interdependent and structured output spaces. - Ioannis Tsochantaridis; Thomas Hofmann 001; Thorsten Joachims; Yasemin Altun (ICML, 2004)

Eye-tracking analysis of user behavior in WWW search. - Laura A. Granka; Thorsten Joachims; Geri Gay (SIGIR, 2004)

KDD-Cup 2004: results and analysis. - Rich Caruana; Thorsten Joachims; Lars Backstrom (SIGKDD Explor., 2004)

Supervised clustering with support vector machines. - Thomas Finley; Thorsten Joachims (ICML, 2005)

A support vector method for multivariate performance measures. - Thorsten Joachims (ICML, 2005)

Error bounds for correlation clustering. - Thorsten Joachims; John E. Hopcroft (ICML, 2005)

Query chains: learning to rank from implicit feedback. - Filip Radlinski; Thorsten Joachims (KDD, 2005)

Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation. - Carmel Domshlak; Thorsten Joachims (UAI, 2005)

Large Margin Methods for Structured and Interdependent Output Variables. - Ioannis Tsochantaridis; Thorsten Joachims; Thomas Hofmann 001; Yasemin Altun (J. Mach. Learn. Res., 2005)

Transductive Support Vector Machines. - Thorsten Joachims (Semi-Supervised Learning, 2006)

Minimally Invasive Randomization fro Collecting Unbiased Preferences from Clickthrough Logs. - Filip Radlinski; Thorsten Joachims (AAAI, 2006)

Training linear SVMs in linear time. - Thorsten Joachims (KDD, 2006)

Structured Output Prediction with Support Vector Machines. - Thorsten Joachims (SSPR/SPR, 2006)

Evaluating the Robustness of Learning from Implicit Feedback - Filip Radlinski; Thorsten Joachims (CoRR, 2006)

Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs - Filip Radlinski; Thorsten Joachims (CoRR, 2006)

The influence of task and gender on search and evaluation behavior using Google. - Lori Lorigo; Bing Pan; Helene Hembrooke; Thorsten Joachims; Laura A. Granka; Geri Gay (Inf. Process. Manag., 2006)

Recommending related papers based on digital library access records. - Stefan Pohl; Filip Radlinski; Thorsten Joachims (JCDL, 2007)

Active exploration for learning rankings from clickthrough data. - Filip Radlinski; Thorsten Joachims (KDD, 2007)

Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases. - Benyah Shaparenko; Thorsten Joachims (KDD, 2007)

A support vector method for optimizing average precision. - Yisong Yue; Thomas Finley; Filip Radlinski; Thorsten Joachims (SIGIR, 2007)

Search Engines that Learn from Implicit Feedback. - Thorsten Joachims; Filip Radlinski (Computer, 2007)

In Google We Trust: Users' Decisions on Rank, Position, and Relevance. - Bing Pan; Helene Hembrooke; Thorsten Joachims; Lori Lorigo; Geri Gay; Laura A. Granka (J. Comput. Mediat. Commun., 2007)

Learning to rank for information retrieval (LR4IR 2007). - Thorsten Joachims; Hang Li 001; Tie-Yan Liu; ChengXiang Zhai (SIGIR Forum, 2007)

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search. - Thorsten Joachims; Laura A. Granka; Bing Pan; Helene Hembrooke; Filip Radlinski; Geri Gay (ACM Trans. Inf. Syst., 2007)

Efficient and non-parametric reasoning over user preferences. - Carmel Domshlak; Thorsten Joachims (User Model. User Adapt. Interact., 2007)

How does clickthrough data reflect retrieval quality? - Filip Radlinski; Madhu Kurup; Thorsten Joachims (CIKM, 2008)

Training structural SVMs when exact inference is intractable. - Thomas Finley; Thorsten Joachims (ICML, 2008)

Learning diverse rankings with multi-armed bandits. - Filip Radlinski; Robert Kleinberg; Thorsten Joachims (ICML, 2008)

Predicting diverse subsets using structural SVMs. - Yisong Yue; Thorsten Joachims (ICML, 2008)

Training structural svms with kernels using sampled cuts. - Chun-Nam John Yu; Thorsten Joachims (KDD, 2008)

Invited speaker: Structured Output Prediction with Structural SVMs. - Thorsten Joachims (MLG@ICML, 2008)

Eye tracking and online search: Lessons learned and challenges ahead. - Lori Lorigo; Maya Haridasan; Hrönn Brynjarsdóttir; Ling Xia; Thorsten Joachims; Geri Gay; Laura A. Granka; Fabio Pellacini; Bing Pan (J. Assoc. Inf. Sci. Technol., 2008)

Support Vector Training of Protein Alignment Models. - Chun-Nam John Yu; Thorsten Joachims; Ron Elber; Jaroslaw Pillardy (J. Comput. Biol., 2008)

Text categorization. - Yiming Yang; Thorsten Joachims (Scholarpedia, 2008)

Beyond binary relevance: preferences, diversity, and set-level judgments. - Paul N. Bennett; Ben Carterette; Olivier Chapelle; Thorsten Joachims (SIGIR Forum, 2008)

Learning structural SVMs with latent variables. - Chun-Nam John Yu; Thorsten Joachims (ICML, 2009)

Interactively optimizing information retrieval systems as a dueling bandits problem. - Yisong Yue; Thorsten Joachims (ICML, 2009)

Identifying the original contribution of a document via language modeling. - Benyah Shaparenko; Thorsten Joachims (SIGIR, 2009)

Predicting structured objects with support vector machines. - Thorsten Joachims; Thomas Hofmann 001; Yisong Yue; Chun-Nam John Yu (Commun. ACM, 2009)

Cutting-plane training of structural SVMs. - Thorsten Joachims; Thomas Finley; Chun-Nam John Yu (Mach. Learn., 2009)

Sparse kernel SVMs via cutting-plane training. - Thorsten Joachims; Chun-Nam John Yu (Mach. Learn., 2009)

Redundancy, diversity and interdependent document relevance. - Filip Radlinski; Paul N. Bennett; Ben Carterette; Thorsten Joachims (SIGIR Forum, 2009)

Evaluating Search Engine Relevance with Click-Based Metrics. - Filip Radlinski; Madhu Kurup; Thorsten Joachims (Preference Learning, 2010)

Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes. - Zhao Xu 001; Kristian Kersting; Thorsten Joachims (ECML/PKDD (3), 2010)

Learning more powerful test statistics for click-based retrieval evaluation. - Yisong Yue; Yue Gao 005; Olivier Chapelle; Ya Zhang 002; Thorsten Joachims (SIGIR, 2010)

Introduction to special issue on learning to rank for information retrieval. - Tie-Yan Liu; Thorsten Joachims; Hang Li 001; Chengxiang Zhai (Inf. Retr., 2010)

Structured learning of two-level dynamic rankings. - Karthik Raman 001; Thorsten Joachims; Pannaga Shivaswamy (CIKM, 2011)

The Value of User Feedback. - Thorsten Joachims (ECIR, 2011)

Beat the Mean Bandit. - Yisong Yue; Thorsten Joachims (ICML, 2011)

Semantic Labeling of 3D Point Clouds for Indoor Scenes. - Hema Swetha Koppula; Abhishek Anand; Thorsten Joachims; Ashutosh Saxena (NIPS, 2011)

Dynamic ranked retrieval. - Christina Brandt; Thorsten Joachims; Yisong Yue; Jacob Bank (WSDM, 2011)

Labeling 3D scenes for Personal Assistant Robots - Hema Swetha Koppula; Abhishek Anand; Thorsten Joachims; Ashutosh Saxena (CoRR, 2011)

Large-Margin Learning of Submodular Summarization Methods - Ruben Sipos; Pannaga Shivaswamy; Thorsten Joachims (CoRR, 2011)

Online Learning with Preference Feedback - Pannagadatta K. Shivaswamy; Thorsten Joachims (CoRR, 2011)

Contextually Guided Semantic Labeling and Search for 3D Point Clouds - Abhishek Anand; Hema Swetha Koppula; Thorsten Joachims; Ashutosh Saxena (CoRR, 2011)

Enriching information retrieval. - Paul N. Bennett; Khalid El-Arini; Thorsten Joachims; Krysta M. Svore (SIGIR Forum, 2011)

Temporal corpus summarization using submodular word coverage. - Ruben Sipos; Adith Swaminathan; Pannaga Shivaswamy; Thorsten Joachims (CIKM, 2012)

Large-Margin Learning of Submodular Summarization Models. - Ruben Sipos; Pannaga Shivaswamy; Thorsten Joachims (EACL, 2012)

Online Structured Prediction via Coactive Learning. - Pannaga Shivaswamy; Thorsten Joachims (ICML, 2012)

Learning to Embed Songs and Tags for Playlist Prediction. - Joshua L. Moore; Shuo Chen 008; Thorsten Joachims; Douglas R. Turnbull (ISMIR, 2012)

Playlist prediction via metric embedding. - Shuo Chen 008; Joshua L. Moore; Douglas R. Turnbull; Thorsten Joachims (KDD, 2012)

Online learning to diversify from implicit feedback. - Karthik Raman 001; Pannaga Shivaswamy; Thorsten Joachims (KDD, 2012)

The K-armed dueling bandits problem. - Yisong Yue; Josef Broder; Robert Kleinberg; Thorsten Joachims (J. Comput. Syst. Sci., 2012)

Multi-armed Bandit Problems with History. - Pannagadatta K. Shivaswamy; Thorsten Joachims (AISTATS, 2012)

Large-scale validation and analysis of interleaved search evaluation. - Olivier Chapelle; Thorsten Joachims; Filip Radlinski; Yisong Yue (ACM Trans. Inf. Syst., 2012)

Maschinelles Lernen und Data Mining. - Stefan Wrobel; Thorsten Joachims; Katharina Morik (Handbuch der Künstlichen Intelligenz, 2013)

Generating comparative summaries from reviews. - Ruben Sipos; Thorsten Joachims (CIKM, 2013)

Structured Learning of Sum-of-Submodular Higher Order Energy Functions. - Alexander Fix; Thorsten Joachims; Sung Min Park 002; Ramin Zabih (ICCV, 2013)

Stable Coactive Learning via Perturbation. - Karthik Raman 001; Thorsten Joachims; Pannaga Shivaswamy; Tobias Schnabel (ICML (3), 2013)

Taste Over Time: The Temporal Dynamics of User Preferences. - Joshua L. Moore; Shuo Chen 008; Douglas R. Turnbull; Thorsten Joachims (ISMIR, 2013)

Multi-space probabilistic sequence modeling. - Shuo Chen 008; Jiexun Xu; Thorsten Joachims (KDD, 2013)

Beyond myopic inference in big data pipelines. - Karthik Raman 001; Adith Swaminathan; Johannes Gehrke; Thorsten Joachims (KDD, 2013)

Learning Trajectory Preferences for Manipulators via Iterative Improvement. - Ashesh Jain; Brian Wojcik; Thorsten Joachims; Ashutosh Saxena (NIPS, 2013)

Learning Socially Optimal Information Systems from Egoistic Users. - Karthik Raman 001; Thorsten Joachims (ECML/PKDD (2), 2013)

Structured learning of sum-of-submodular higher order energy functions. - Alexander Fix; Thorsten Joachims; Sam Park; Ramin Zabih (CoRR, 2013)

Learning Trajectory Preferences for Manipulators via Iterative Improvement. - Ashesh Jain; Thorsten Joachims; Ashutosh Saxena (CoRR, 2013)

Contextually guided semantic labeling and search for three-dimensional point clouds. - Abhishek Anand; Hema Swetha Koppula; Thorsten Joachims; Ashutosh Saxena (Int. J. Robotics Res., 2013)

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 (CHI Extended Abstracts, 2014)

Invited Talk: Learning from Rational Behavior. - Thorsten Joachims (EMNLP, 2014)

Reducing Dueling Bandits to Cardinal Bandits. - Nir Ailon; Zohar Shay Karnin; Thorsten Joachims (ICML, 2014)

Taste Space Versus the World: an Embedding Analysis of Listening Habits and Geography. - Joshua L. Moore; Thorsten Joachims; Douglas R. Turnbull (ISMIR, 2014)

Methods for ordinal peer grading. - Karthik Raman 001; Thorsten Joachims (KDD, 2014)

Was this review helpful to you?: it depends! context and voting patterns in online content. - Ruben Sipos; Arpita Ghosh; Thorsten Joachims (WWW, 2014)

Behavior Informatics: A New Perspective. - Longbing Cao; Thorsten Joachims; Can Wang 004; Éric Gaussier; Jinjiu Li; Yuming Ou; Dan Luo 001; Reza Zafarani; Huan Liu 001; Guandong Xu; Zhiang Wu 001; Gabriella Pasi; et al. (IEEE Intell. Syst., 2014)

Evaluation methods for unsupervised word embeddings. - Tobias Schnabel; Igor Labutov; David M. Mimno; Thorsten Joachims (EMNLP, 2015)

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. - Adith Swaminathan; Thorsten Joachims (ICML, 2015)

Bayesian Ordinal Peer Grading. - Karthik Raman 001; Thorsten Joachims (L@S, 2015)

The Self-Normalized Estimator for Counterfactual Learning. - Adith Swaminathan; Thorsten Joachims (NIPS, 2015)

Learning from User Interactions. - Thorsten Joachims (WSDM, 2015)

Unbiased Ranking Evaluation on a Budget. - Tobias Schnabel; Adith Swaminathan; Thorsten Joachims (WWW (Companion Volume), 2015)

Counterfactual Risk Minimization. - Adith Swaminathan; Thorsten Joachims (WWW (Companion Volume), 2015)

Mapping preferences into Euclidean space. - Oscar Luaces; Jorge Díez 001; Thorsten Joachims; Antonio Bahamonde (Expert Syst. Appl., 2015)

Learning preferences for manipulation tasks from online coactive feedback. - Ashesh Jain; Shikhar Sharma 001; Thorsten Joachims; Ashutosh Saxena (Int. J. Robotics Res., 2015)

Coactive Learning. - Pannaga Shivaswamy; Thorsten Joachims (J. Artif. Intell. Res., 2015)

Batch learning from logged bandit feedback through counterfactual risk minimization. - Adith Swaminathan; Thorsten Joachims (J. Mach. Learn. Res., 2015)

Analysis of nutrition data by means of a matrix factorization method. - Jorge Díez 001; Edna Gamboa; Teresita González de Cossío; Oscar Luaces; Thorsten Joachims; Antonio Bahamonde (Prog. Artif. Intell., 2015)

Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015. - Thorsten Joachims; Karthik Raman 001 (Solving Large Scale Learning Tasks, 2016)

Recommendations as Treatments: Debiasing Learning and Evaluation. - Tobias Schnabel; Adith Swaminathan; Ashudeep Singh; Navin Chandak; Thorsten Joachims (ICML, 2016)

Unbiased Comparative Evaluation of Ranking Functions. - Tobias Schnabel; Adith Swaminathan; Peter I. Frazier; Thorsten Joachims (ICTIR, 2016)

Predicting Matchups and Preferences in Context. - Shuo Chen 008; Thorsten Joachims (KDD, 2016)

Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. - Siddharth Reddy; Igor Labutov; Siddhartha Banerjee; Thorsten Joachims (KDD, 2016)

Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation. - Siddharth Reddy; Igor Labutov; Thorsten Joachims (L@S, 2016)

Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. - Thorsten Joachims; Adith Swaminathan (SIGIR, 2016)

Modeling Intransitivity in Matchup and Comparison Data. - Shuo Chen 008; Thorsten Joachims (WSDM, 2016)

Using Shortlists to Support Decision Making and Improve Recommender System Performance. - Tobias Schnabel; Paul N. Bennett; Susan T. Dumais; Thorsten Joachims (WWW, 2016)

Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. - Damien Lefortier; Adith Swaminathan; Xiaotao Gu; Thorsten Joachims; Maarten de Rijke (CoRR, 2016)

Latent Skill Embedding for Personalized Lesson Sequence Recommendation. - Siddharth Reddy; Igor Labutov; Thorsten Joachims (CoRR, 2016)

Ranking with Social Cues: Integrating Online Review Scores and Popularity Information. - Pantelis P. Analytis; Alexia Delfino; Juliane E. Kämmer; Mehdi Moussaïd; Thorsten Joachims (ICWSM, 2017)

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. - Aman Agarwal; Soumya Basu 003; Tobias Schnabel; Thorsten Joachims (KDD, 2017)

A preference elicitation interface for collecting dense recommender datasets with rich user information. - Pantelis P. Analytis; Tobias Schnabel; Stefan M. Herzog; Daniel Barkoczi; Thorsten Joachims (CoRR, 2017)

Accurately Interpreting Clickthrough Data as Implicit Feedback. - Thorsten Joachims; Laura A. Granka; Bing Pan; Helene Hembrooke; Geri Gay (SIGIR Forum, 2017)

Deep Learning with Logged Bandit Feedback. - Thorsten Joachims; Adith Swaminathan; Maarten de Rijke (ICLR (Poster), 2018)

Unbiased Learning-to-Rank with Biased Feedback. - Thorsten Joachims; Adith Swaminathan; Tobias Schnabel (IJCAI, 2018)

Fairness of Exposure in Rankings. - Ashudeep Singh; Thorsten Joachims (KDD, 2018)

Deep Learning from Logged Interventions. - Thorsten Joachims (DLRS@RecSys, 2018)

REVEAL 2018: offline evaluation for recommender systems. - Thorsten Joachims; Adith Swaminathan; Yves Raimond; Olivier Koch; Flavian Vasile (RecSys, 2018)

Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. - Tobias Schnabel; Paul N. Bennett; Susan T. Dumais; Thorsten Joachims (WSDM, 2018)

Improving Recommender Systems Beyond the Algorithm. - Tobias Schnabel; Paul N. Bennett; Thorsten Joachims (CoRR, 2018)

Counterfactual Learning-to-Rank for Additive Metrics and Deep Models. - Aman Agarwal; Ivan Zaitsev; Thorsten Joachims (CoRR, 2018)

Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank. - Aman Agarwal; Ivan Zaitsev; Thorsten Joachims (CoRR, 2018)

CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning. - Yi Su; Lequn Wang; Michele Santacatterina; Thorsten Joachims (CoRR, 2018)

Extreme Classification (Dagstuhl Seminar 18291). - Samy Bengio; Krzysztof Dembczynski; Thorsten Joachims; Marius Kloft; Manik Varma (Dagstuhl Reports, 2018)

CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. - Yi Su; Lequn Wang; Michele Santacatterina; Thorsten Joachims (ICML, 2019)

Policy Learning for Fairness in Ranking. - Ashudeep Singh; Thorsten Joachims (NeurIPS, 2019)

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 (RecSys, 2019)

A General Framework for Counterfactual Learning-to-Rank. - Aman Agarwal; Kenta Takatsu; Ivan Zaitsev; Thorsten Joachims (SIGIR, 2019)

Intervention Harvesting for Context-Dependent Examination-Bias Estimation. - Zhichong Fang; Aman Agarwal; Thorsten Joachims (SIGIR, 2019)

Estimating Position Bias without Intrusive Interventions. - Aman Agarwal; Ivan Zaitsev; Xuanhui Wang; Cheng Li 012; Marc Najork; Thorsten Joachims (WSDM, 2019)

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. - Tobias Schnabel; Paul N. Bennett; Thorsten Joachims (WSDM, 2019)

Fair Learning-to-Rank from Implicit Feedback. - Himank Yadav; Zhengxiao Du; Thorsten Joachims (CoRR, 2019)

Exploring Acoustic Similarity for Novel Music Recommendation. - Derek Cheng; Thorsten Joachims; Douglas R. Turnbull (ISMIR, 2020)

Off-policy Bandits with Deficient Support. - Noveen Sachdeva; Yi Su; Thorsten Joachims (KDD, 2020)

MOReL: Model-Based Offline Reinforcement Learning. - Rahul Kidambi; Aravind Rajeswaran; Praneeth Netrapalli; Thorsten Joachims (NeurIPS, 2020)

REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. - Thorsten Joachims; Yves Raimond; Olivier Koch; Maria Dimakopoulou; Flavian Vasile; Adith Swaminathan (RecSys, 2020)

Controlling Fairness and Bias in Dynamic Learning-to-Rank. - Marco Morik; Ashudeep Singh; Jessica Hong; Thorsten Joachims (SIGIR, 2020)

The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. - Tobias Schnabel; Saleema Amershi; Paul N. Bennett; Peter Bailey; Thorsten Joachims (SIGIR, 2020)

Content-based Music Similarity with Triplet Networks. - Joseph Cleveland; Derek Cheng; Michael Zhou; Thorsten Joachims; Douglas R. Turnbull (CoRR, 2020)

Fairness and Diversity for Rankings in Two-Sided Markets. - Lequn Wang; Thorsten Joachims (CoRR, 2020)

An Interview with Dr. Thorsten Joachims, Winner of ACM SIGKDD 2020 Innovation Award. - Thorsten Joachims (SIGKDD Explor., 2020)

Fairness of Exposure in Stochastic Bandits. - Lequn Wang; Yiwei Bai; Wen Sun 002; Thorsten Joachims (ICML, 2021)

Fairness and Control of Exposure in Two-sided Markets. - Thorsten Joachims (ICTIR, 2021)

User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. - Lequn Wang; Thorsten Joachims (ICTIR, 2021)

Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract). - Marco Morik; Ashudeep Singh; Jessica Hong; Thorsten Joachims (IJCAI, 2021)

Fairness in Ranking under Uncertainty. - Ashudeep Singh; David Kempe 001; Thorsten Joachims (NeurIPS, 2021)

Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances. - Yuta Saito; Thorsten Joachims (RecSys, 2021)

Policy-Gradient Training of Fair and Unbiased Ranking Functions. - Himank Yadav; Zhengxiao Du; Thorsten Joachims (SIGIR, 2021)

Recommendations as Treatments. - Thorsten Joachims; Ben London 001; Yi Su; Adith Swaminathan; Lequn Wang (AI Mag., 2021)

Off-Policy Evaluation for Large Action Spaces via Embeddings. - Yuta Saito; Thorsten Joachims (ICML, 2022)

Improving Screening Processes via Calibrated Subset Selection. - Lequn Wang; Thorsten Joachims; Manuel Gomez Rodriguez (ICML, 2022)

Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. - Yuta Saito; Thorsten Joachims (KDD, 2022)

Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances. - Yuta Saito; Thorsten Joachims (KDD, 2022)

CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. - Olivier Jeunen; Thorsten Joachims; Harrie Oosterhuis; Yuta Saito; Flavian Vasile (RecSys, 2022)

Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. - Adam Block; Rahul Kidambi; Daniel N. Hill; Thorsten Joachims; Inderjit S. Dhillon (SIGIR, 2022)

Optimizing Rankings for Recommendation in Matching Markets. - Yi Su; Magd Bayoumi; Thorsten Joachims (WWW, 2022)

Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. - Aaron David Tucker; Thorsten Joachims (CoRR, 2022)

Fairness in the First Stage of Two-Stage Recommender Systems. - Lequn Wang; Thorsten Joachims (CoRR, 2022)

Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model. - Alexander Buchholz; Ben London 001; Giuseppe Di Benedetto; Thorsten Joachims (CoRR, 2022)

Boosted Off-Policy Learning. - Ben London 001; Levi Lu; Ted Sandler; Thorsten Joachims (AISTATS, 2023)

How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making. - Joyce Zhou; Thorsten Joachims (FAccT, 2023)

Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. - Yuta Saito; Qingyang Ren; Thorsten Joachims (ICML, 2023)

Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. - Hansol Lee; René F. Kizilcec; Thorsten Joachims (L@S, 2023)

Contextual Position Bias Estimation Using a Single Stochastic Logging Policy. - Giuseppe Di Benedetto; Alexander Buchholz; Ben London 001; Matej Jakimov; Yannik Stein; Jan Malte Lichtenberg; Vito Bellini; Matteo Ruffini; Thorsten Joachims (LERI@RecSys, 2023)

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 002 (RecSys, 2023)

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 (RecSys, 2023)

Bandits with costly reward observations. - Aaron David Tucker; Caleb Biddulph; Claire Wang; Thorsten Joachims (UAI, 2023)

Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. - Aaron David Tucker; Thorsten Joachims (WSDM, 2023)

Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. - Lequn Wang; Thorsten Joachims (WSDM, 2023)

Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters. - Jinsook Lee; Bradon Thymes; Joyce Zhou; Thorsten Joachims; René F. Kizilcec (CoRR, 2023)

Fair Ranking under Disparate Uncertainty. - Richa Rastogi; Thorsten Joachims (CoRR, 2023)

GPT as a Baseline for Recommendation Explanation Texts. - Joyce Zhou; Thorsten Joachims (CoRR, 2023)

Policy-Gradient Training of Language Models for Ranking. - Ge Gao; Jonathan D. Chang; Claire Cardie; Kianté Brantley; Thorsten Joachims (CoRR, 2023)

Unbiased Offline Evaluation for Learning to Rank with Business Rules. - Matej Jakimov; Alexander Buchholz; Yannik Stein; Thorsten Joachims (CoRR, 2023)

Ending Affirmative Action Harms Diversity Without Improving Academic Merit. - Jinsook Lee; Emma Harvey; Joyce Zhou; Nikhil Garg 001; Thorsten Joachims; René F. Kizilcec (EAAMO, 2024)

Fairness in Ranking under Disparate Uncertainty. - Richa Rastogi; Thorsten Joachims (EAAMO, 2024)

Coactive Learning for Large Language Models using Implicit User Feedback. - Aaron David Tucker; Kianté Brantley; Adam Cahall; Thorsten Joachims (ICML, 2024)

Ranking with Slot Constraints. - Wentao Guo; Andrew Wang; Bradon Thymes; Thorsten Joachims (KDD, 2024)

REBEL: Reinforcement Learning via Regressing Relative Rewards. - Zhaolin Gao; Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Gokul Swamy; Kianté Brantley; Thorsten Joachims; Drew Bagnell; Jason D. Lee; Wen Sun 002 (NeurIPS, 2024)

Counterfactual Ranking Evaluation with Flexible Click Models. - Alexander Buchholz; Ben London 001; Giuseppe Di Benedetto; Jan Malte Lichtenberg; Yannik Stein; Thorsten Joachims (SIGIR, 2024)

Ranking with Long-Term Constraints. - Kianté Brantley; Zhichong Fang; Sarah Dean; Thorsten Joachims (WSDM, 2024)

POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition. - Yuta Saito; Jihan Yao; Thorsten Joachims (CoRR, 2024)

Reviewer2: Optimizing Review Generation Through Prompt Generation. - Zhaolin Gao; Kianté Brantley; Thorsten Joachims (CoRR, 2024)

Language-Based User Profiles for Recommendation. - Joyce Zhou; Yijia Dai; Thorsten Joachims (CoRR, 2024)

Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability. - Jinsook Lee; Emma Harvey; Joyce Zhou; Nikhil Garg 001; Thorsten Joachims; René F. Kizilcec (CoRR, 2024)

End-to-end Training for Recommendation with Language-based User Profiles. - Zhaolin Gao; Joyce Zhou; Yijia Dai; Thorsten Joachims (CoRR, 2024)

Large language models, social demography, and hegemony: comparing authorship in human and synthetic text. - A. J. Alvero; Jinsook Lee; Alejandra Regla-Vargas; René F. Kizilcec; Thorsten Joachims; Anthony Lising Antonio (J. Big Data, 2024)

POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition. - Yuta Saito; Jihan Yao; Thorsten Joachims (ICLR, 2025)

MultiScale Contextual Bandits for Long Term Objectives. - Richa Rastogi; Yuta Saito; Thorsten Joachims (CoRR, 2025)

Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays. - Jinsook Lee; A. J. Alvero; Thorsten Joachims; René F. Kizilcec (CoRR, 2025)

Prompt Optimization with Logged Bandit Data. - Haruka Kiyohara; Daniel Yiming Cao; Yuta Saito; Thorsten Joachims (CoRR, 2025)