Thorsten Joachims

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 111

Top Venues

Publications

Jinsook Lee, Emma Harvey, Joyce Zhou, Nikhil Garg, Thorsten Joachims, René F. Kizilcec
Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability. CoRR (2024)
Zhaolin Gao, Kianté Brantley, Thorsten Joachims
Reviewer2: Optimizing Review Generation Through Prompt Generation. CoRR (2024)
Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims
Counterfactual Ranking Evaluation with Flexible Click Models. SIGIR (2024)
Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims
Ranking with Slot Constraints. KDD (2024)
Joyce Zhou, Yijia Dai, Thorsten Joachims
Language-Based User Profiles for Recommendation. CoRR (2024)
Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims
Ranking with Long-Term Constraints. WSDM (2024)
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR (2024)
Yuta Saito, Jihan Yao, Thorsten Joachims
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition. CoRR (2024)
Joyce Zhou, Thorsten Joachims
How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making. FAccT (2023)
Douglas Turnbull, April Trainor, Douglas R. Turnbull, Elizabeth Richards, Kieran Bentley, Victoria Conrad, Paul Gagliano, Cassandra Raineault, Thorsten Joachims
Localify.org: Locally-focus Music Artist and Event Recommendation. RecSys (2023)
Yuta Saito, Qingyang Ren, Thorsten Joachims
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. ICML (2023)
Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
Boosted Off-Policy Learning. AISTATS (2023)
Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims
Ranking with Long-Term Constraints. CoRR (2023)
Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims
Bandits with costly reward observations. UAI (2023)
Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims
Policy-Gradient Training of Language Models for Ranking. CoRR (2023)
Richa Rastogi, Thorsten Joachims
Fair Ranking under Disparate Uncertainty. CoRR (2023)
Matej Jakimov, Alexander Buchholz, Yannik Stein, Thorsten Joachims
Unbiased Offline Evaluation for Learning to Rank with Business Rules. CoRR (2023)
Lequn Wang, Thorsten Joachims
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. WSDM (2023)
Yuta Saito, Qingyang Ren, Thorsten Joachims
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. CoRR (2023)
Jinsook Lee, Bradon Thymes, Joyce Zhou, Thorsten Joachims, René F. Kizilcec
Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters. CoRR (2023)
Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang
CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys (2023)
Giuseppe Di Benedetto, Alexander Buchholz, Ben London, Matej Jakimov, Yannik Stein, Jan Malte Lichtenberg, Vito Bellini, Matteo Ruffini, Thorsten Joachims
Contextual Position Bias Estimation Using a Single Stochastic Logging Policy. LERI@RecSys (2023)
Hansol Lee, René F. Kizilcec, Thorsten Joachims
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. CoRR (2023)
Joyce Zhou, Thorsten Joachims
GPT as a Baseline for Recommendation Explanation Texts. CoRR (2023)
Hansol Lee, René F. Kizilcec, Thorsten Joachims
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. L@S (2023)
Aaron David Tucker, Thorsten Joachims
Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. WSDM (2023)
Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims
Ranking with Slot Constraints. CoRR (2023)
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
Improving Screening Processes via Calibrated Subset Selection. ICML (2022)
Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile
CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys (2022)
Yuta Saito, Thorsten Joachims
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. CoRR (2022)
Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
Boosted Off-Policy Learning. CoRR (2022)
Yi Su, Magd Bayoumi, Thorsten Joachims
Optimizing Rankings for Recommendation in Matching Markets. WWW (2022)
Yuta Saito, Thorsten Joachims
Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances. KDD (2022)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR (2022)
Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Thorsten Joachims
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model. CoRR (2022)
Lequn Wang, Thorsten Joachims
Fairness in the First Stage of Two-Stage Recommender Systems. CoRR (2022)
Yuta Saito, Thorsten Joachims
Off-Policy Evaluation for Large Action Spaces via Embeddings. CoRR (2022)
Yuta Saito, Thorsten Joachims
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. KDD (2022)
Aaron David Tucker, Thorsten Joachims
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. CoRR (2022)
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
Improving Screening Processes via Calibrated Subset Selection. CoRR (2022)
Yuta Saito, Thorsten Joachims
Off-Policy Evaluation for Large Action Spaces via Embeddings. ICML (2022)
Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR (2022)
Thorsten Joachims
Fairness and Control of Exposure in Two-sided Markets. ICTIR (2021)
Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract). IJCAI (2021)
Lequn Wang, Thorsten Joachims
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. ICTIR (2021)
Himank Yadav, Zhengxiao Du, Thorsten Joachims
Policy-Gradient Training of Fair and Unbiased Ranking Functions. SIGIR (2021)
Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang
Recommendations as Treatments. AI Mag. 42 (3) (2021)
Ashudeep Singh, David Kempe, Thorsten Joachims
Fairness in Ranking under Uncertainty. NeurIPS (2021)
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
Fairness of Exposure in Stochastic Bandits. ICML (2021)
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
Fairness of Exposure in Stochastic Bandits. CoRR (2021)
Yi Su, Magd Bayoumi, Thorsten Joachims
Optimizing Rankings for Recommendation in Matching Markets. CoRR (2021)
Yuta Saito, Thorsten Joachims
Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances. RecSys (2021)
Ashudeep Singh, David Kempe, Thorsten Joachims
Fairness in Ranking under Uncertainty. CoRR (2021)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS (2020)
Noveen Sachdeva, Yi Su, Thorsten Joachims
Off-policy Bandits with Deficient Support. KDD (2020)
Noveen Sachdeva, Yi Su, Thorsten Joachims
Off-policy Bandits with Deficient Support. CoRR (2020)
Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. SIGIR (2020)
Derek Cheng, Thorsten Joachims, Douglas Turnbull
Exploring Acoustic Similarity for Novel Music Recommendation. ISMIR (2020)
Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan
REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. RecSys (2020)
Thorsten Joachims
An Interview with Dr. Thorsten Joachims, Winner of ACM SIGKDD 2020 Innovation Award. SIGKDD Explor. 22 (2) (2020)
Joseph Cleveland, Derek Cheng, Michael Zhou, Thorsten Joachims, Douglas Turnbull
Content-based Music Similarity with Triplet Networks. CoRR (2020)
Lequn Wang, Thorsten Joachims
Fairness and Diversity for Rankings in Two-Sided Markets. CoRR (2020)
Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
Controlling Fairness and Bias in Dynamic Learning-to-Rank. SIGIR (2020)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL : Model-Based Offline Reinforcement Learning. CoRR (2020)
Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
Controlling Fairness and Bias in Dynamic Learning-to-Rank. CoRR (2020)
Thorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile
REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. RecSys (2019)
Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims
A General Framework for Counterfactual Learning-to-Rank. SIGIR (2019)
Zhichong Fang, Aman Agarwal, Thorsten Joachims
Intervention Harvesting for Context-Dependent Examination-Bias Estimation. SIGIR (2019)
Himank Yadav, Zhengxiao Du, Thorsten Joachims
Fair Learning-to-Rank from Implicit Feedback. CoRR (2019)
Ashudeep Singh, Thorsten Joachims
Policy Learning for Fairness in Ranking. CoRR (2019)
Ashudeep Singh, Thorsten Joachims
Policy Learning for Fairness in Ranking. NeurIPS (2019)
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims
Estimating Position Bias without Intrusive Interventions. WSDM (2019)
Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. WSDM (2019)
Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. ICML (2019)
Samy Bengio, Krzysztof Dembczynski, Thorsten Joachims, Marius Kloft, Manik Varma
Extreme Classification (Dagstuhl Seminar 18291). Dagstuhl Reports 8 (7) (2018)
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims
Estimating Position Bias without Intrusive Interventions. CoRR (2018)
Zhichong Fang, Aman Agarwal, Thorsten Joachims
Intervention Harvesting for Context-Dependent Examination-Bias Estimation. CoRR (2018)
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Unbiased Learning-to-Rank with Biased Feedback. IJCAI (2018)
Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning. CoRR (2018)
Ashudeep Singh, Thorsten Joachims
Fairness of Exposure in Rankings. KDD (2018)
Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. WSDM (2018)
Thorsten Joachims
Deep Learning from Logged Interventions. DLRS@RecSys (2018)
Aman Agarwal, Ivan Zaitsev, Thorsten Joachims
Counterfactual Learning-to-Rank for Additive Metrics and Deep Models. CoRR (2018)
Ashudeep Singh, Thorsten Joachims
Fairness of Exposure in Rankings. CoRR (2018)
Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile
REVEAL 2018: offline evaluation for recommender systems. RecSys (2018)
Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
Deep Learning with Logged Bandit Feedback. ICLR (Poster) (2018)
Aman Agarwal, Ivan Zaitsev, Thorsten Joachims
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank. CoRR (2018)
Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Improving Recommender Systems Beyond the Algorithm. CoRR (2018)
Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers. CoRR (2017)
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Unbiased Learning-to-Rank with Biased Feedback. WSDM (2017)
Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay
Accurately Interpreting Clickthrough Data as Implicit Feedback. SIGIR Forum 51 (1) (2017)
Pantelis P. Analytis, Tobias Schnabel, Stefan Herzog, Daniel Barkoczi, Thorsten Joachims
A preference elicitation interface for collecting dense recommender datasets with rich user information. CoRR (2017)
Pantelis P. Analytis, Alexia Delfino, Juliane E. Kämmer, Mehdi Moussaïd, Thorsten Joachims
Ranking with Social Cues: Integrating Online Review Scores and Popularity Information. ICWSM (2017)
Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. KDD (2017)
Pantelis P. Analytis, Alexia Delfino, Juliane E. Kämmer, Mehdi Moussaïd, Thorsten Joachims
Ranking with social cues: Integrating online review scores and popularity information. CoRR (2017)
Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena
Learning Preferences for Manipulation Tasks from Online Coactive Feedback. CoRR (2016)
Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. CoRR (2016)
Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
Using Shortlists to Support Decision Making and Improve Recommender System Performance. WWW (2016)
Thorsten Joachims, Karthik Raman
Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015. Solving Large Scale Learning Tasks (2016)
Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. CoRR (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Thorsten Joachims