Thorsten Joachims

272 papers A* 78A 33B 4Misc 3Journal 117Unranked 30

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review. CoRR Vibhhu Sharma, Thorsten Joachims, Sarah Dean
2026	J	`jnl`	Does Algorithmic Uncertainty Sway Human Experts? Evidence from a Field Experiment in Selective College Admissions. CoRR Hansol Lee, A. J. Alvero, René F. Kizilcec, Thorsten Joachims
2026	J	`jnl`	SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation. CoRR Joyce Zhou, Weijie Zhou, Douglas Turnbull, Thorsten Joachims
2026	J	`jnl`	The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essays. CoRR Jinsook Lee, Conrad Borchers, A. J. Alvero, Thorsten Joachims, René F. Kizilcec
2025	A	`conf`	An Off-Policy Learning Approach for Steering Sentence Generation towards Personalization. RecSys Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims
2025	A	`conf`	CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys Harrie Oosterhuis, Olivier Jeunen, Yuta Saito, Yixin Wang, Flavian Vasile, Thorsten Joachims
2025	A	`conf`	LangPTune: Optimizing Language-based User Profiles for Recommendation. CIKM Zhaolin Gao, Joyce Zhou, Yijia Dai, Thorsten Joachims
2025	J	`jnl`	MultiScale Contextual Bandits for Long Term Objectives. CoRR Richa Rastogi, Yuta Saito, Thorsten Joachims
2025	A*	`conf`	POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition. ICLR Yuta Saito, Jihan Yao, Thorsten Joachims
2025	J	`jnl`	Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays. CoRR Jinsook Lee, A. J. Alvero, Thorsten Joachims, René F. Kizilcec
2025	J	`jnl`	Prompt Curriculum Learning for Efficient LLM Post-Training. CoRR Zhaolin Gao, Joongwon Kim, Wen Sun, Thorsten Joachims, Sid Wang, Richard Yuanzhe Pang, Liang Tan
2025	J	`jnl`	Prompt Optimization with Logged Bandit Data. CoRR Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito, Thorsten Joachims
2024	J	`jnl`	Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability. CoRR Jinsook Lee, Emma Harvey, Joyce Zhou, Nikhil Garg, Thorsten Joachims, René F. Kizilcec
2024	A*	`conf`	Coactive Learning for Large Language Models using Implicit User Feedback. ICML Aaron David Tucker, Kianté Brantley, Adam Cahall, Thorsten Joachims
2024	A*	`conf`	Counterfactual Ranking Evaluation with Flexible Click Models. SIGIR Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims
2024	J	`jnl`	End-to-end Training for Recommendation with Language-based User Profiles. CoRR Zhaolin Gao, Joyce Zhou, Yijia Dai, Thorsten Joachims
2024	—	`conf`	Ending Affirmative Action Harms Diversity Without Improving Academic Merit. EAAMO Jinsook Lee, Emma Harvey, Joyce Zhou, Nikhil Garg, Thorsten Joachims, René F. Kizilcec
2024	—	`conf`	Fairness in Ranking under Disparate Uncertainty. EAAMO Richa Rastogi, Thorsten Joachims
2024	J	`jnl`	Language-Based User Profiles for Recommendation. CoRR Joyce Zhou, Yijia Dai, Thorsten Joachims
2024	J	`jnl`	Large language models, social demography, and hegemony: comparing authorship in human and synthetic text. J. Big Data A. J. Alvero, Jinsook Lee, Alejandra Regla-Vargas, René F. Kizilcec, Thorsten Joachims, Anthony Lising Antonio
2024	J	`jnl`	POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition. CoRR Yuta Saito, Jihan Yao, Thorsten Joachims
2024	A	`ed.`	Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024 RecSys Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, Ben London
2024	A*	`conf`	REBEL: Reinforcement Learning via Regressing Relative Rewards. NeurIPS Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, Drew Bagnell, Jason D. Lee, Wen Sun
2024	J	`jnl`	REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
2024	A	`conf`	Ranking with Long-Term Constraints. WSDM Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims
2024	A*	`conf`	Ranking with Slot Constraints. KDD Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims
2024	J	`jnl`	Reviewer2: Optimizing Review Generation Through Prompt Generation. CoRR Zhaolin Gao, Kianté Brantley, Thorsten Joachims
2023	J	`jnl`	Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters. CoRR Jinsook Lee, Bradon Thymes, Joyce Zhou, Thorsten Joachims, René F. Kizilcec
2023	A	`conf`	Bandits with costly reward observations. UAI Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims
2023	A	`conf`	Boosted Off-Policy Learning. AISTATS Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
2023	A	`conf`	CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang
2023	—	`conf`	Contextual Position Bias Estimation Using a Single Stochastic Logging Policy. LERI@RecSys Giuseppe Di Benedetto, Alexander Buchholz, Ben London, Matej Jakimov, Yannik Stein, Jan Malte Lichtenberg, Vito Bellini, Matteo Ruffini, Thorsten Joachims
2023	B	`conf`	Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. L@S Hansol Lee, René F. Kizilcec, Thorsten Joachims
2023	J	`jnl`	Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. CoRR Hansol Lee, René F. Kizilcec, Thorsten Joachims
2023	J	`jnl`	Fair Ranking under Disparate Uncertainty. CoRR Richa Rastogi, Thorsten Joachims
2023	J	`jnl`	GPT as a Baseline for Recommendation Explanation Texts. CoRR Joyce Zhou, Thorsten Joachims
2023	—	`conf`	How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making. FAccT Joyce Zhou, Thorsten Joachims
2023	A	`conf`	Localify.org: Locally-focus Music Artist and Event Recommendation. RecSys Douglas Turnbull, April Trainor, Douglas R. Turnbull, Elizabeth Richards, Kieran Bentley, Victoria Conrad, Paul Gagliano, Cassandra Raineault, Thorsten Joachims
2023	A*	`conf`	Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. ICML Yuta Saito, Qingyang Ren, Thorsten Joachims
2023	J	`jnl`	Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. CoRR Yuta Saito, Qingyang Ren, Thorsten Joachims
2023	J	`jnl`	Policy-Gradient Training of Language Models for Ranking. CoRR Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims
2023	J	`jnl`	Ranking with Long-Term Constraints. CoRR Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims
2023	J	`jnl`	Ranking with Slot Constraints. CoRR Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims
2023	J	`jnl`	Unbiased Offline Evaluation for Learning to Rank with Business Rules. CoRR Matej Jakimov, Alexander Buchholz, Yannik Stein, Thorsten Joachims
2023	A	`conf`	Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. WSDM Lequn Wang, Thorsten Joachims
2023	A	`conf`	Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. WSDM Aaron David Tucker, Thorsten Joachims
2022	J	`jnl`	Boosted Off-Policy Learning. CoRR Ben London, Levi Lu, Ted Sandler, Thorsten Joachims
2022	A	`conf`	CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile
2022	A*	`conf`	Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances. KDD Yuta Saito, Thorsten Joachims
2022	A*	`conf`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2022	J	`jnl`	Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon
2022	A*	`conf`	Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. KDD Yuta Saito, Thorsten Joachims
2022	J	`jnl`	Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. CoRR Yuta Saito, Thorsten Joachims
2022	J	`jnl`	Fairness in the First Stage of Two-Stage Recommender Systems. CoRR Lequn Wang, Thorsten Joachims
2022	A*	`conf`	Improving Screening Processes via Calibrated Subset Selection. ICML Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
2022	J	`jnl`	Improving Screening Processes via Calibrated Subset Selection. CoRR Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
2022	A*	`conf`	Off-Policy Evaluation for Large Action Spaces via Embeddings. ICML Yuta Saito, Thorsten Joachims
2022	J	`jnl`	Off-Policy Evaluation for Large Action Spaces via Embeddings. CoRR Yuta Saito, Thorsten Joachims
2022	J	`jnl`	Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model. CoRR Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Thorsten Joachims
2022	A*	`conf`	Optimizing Rankings for Recommendation in Matching Markets. WWW Yi Su, Magd Bayoumi, Thorsten Joachims
2022	J	`jnl`	Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. CoRR Aaron David Tucker, Thorsten Joachims
2021	A*	`conf`	Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract). IJCAI Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
2021	A	`conf`	Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances. RecSys Yuta Saito, Thorsten Joachims
2021	Misc	`conf`	Fairness and Control of Exposure in Two-sided Markets. ICTIR Thorsten Joachims
2021	A*	`conf`	Fairness in Ranking under Uncertainty. NeurIPS Ashudeep Singh, David Kempe, Thorsten Joachims
2021	J	`jnl`	Fairness in Ranking under Uncertainty. CoRR Ashudeep Singh, David Kempe, Thorsten Joachims
2021	A*	`conf`	Fairness of Exposure in Stochastic Bandits. ICML Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
2021	J	`jnl`	Fairness of Exposure in Stochastic Bandits. CoRR Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
2021	J	`jnl`	Optimizing Rankings for Recommendation in Matching Markets. CoRR Yi Su, Magd Bayoumi, Thorsten Joachims
2021	A*	`conf`	Policy-Gradient Training of Fair and Unbiased Ranking Functions. SIGIR Himank Yadav, Zhengxiao Du, Thorsten Joachims
2021	J	`jnl`	Recommendations as Treatments. AI Mag. Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang
2021	Misc	`conf`	User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. ICTIR Lequn Wang, Thorsten Joachims
2020	J	`jnl`	An Interview with Dr. Thorsten Joachims, Winner of ACM SIGKDD 2020 Innovation Award. SIGKDD Explor. Thorsten Joachims
2020	J	`jnl`	Content-based Music Similarity with Triplet Networks. CoRR Joseph Cleveland, Derek Cheng, Michael Zhou, Thorsten Joachims, Douglas R. Turnbull
2020	A*	`conf`	Controlling Fairness and Bias in Dynamic Learning-to-Rank. SIGIR Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
2020	J	`jnl`	Controlling Fairness and Bias in Dynamic Learning-to-Rank. CoRR Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims
2020	—	`conf`	Exploring Acoustic Similarity for Novel Music Recommendation. ISMIR Derek Cheng, Thorsten Joachims, Douglas R. Turnbull
2020	J	`jnl`	Fairness and Diversity for Rankings in Two-Sided Markets. CoRR Lequn Wang, Thorsten Joachims
2020	J	`jnl`	MOReL : Model-Based Offline Reinforcement Learning. CoRR Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2020	A*	`conf`	MOReL: Model-Based Offline Reinforcement Learning. NeurIPS Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2020	A*	`conf`	Off-policy Bandits with Deficient Support. KDD Noveen Sachdeva, Yi Su, Thorsten Joachims
2020	J	`jnl`	Off-policy Bandits with Deficient Support. CoRR Noveen Sachdeva, Yi Su, Thorsten Joachims
2020	A	`conf`	REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. RecSys Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan
2020	A*	`conf`	The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. SIGIR Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims
2019	A*	`conf`	A General Framework for Counterfactual Learning-to-Rank. SIGIR Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims
2019	A*	`conf`	CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. ICML Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
2019	A	`conf`	Estimating Position Bias without Intrusive Interventions. WSDM Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims
2019	J	`jnl`	Fair Learning-to-Rank from Implicit Feedback. CoRR Himank Yadav, Zhengxiao Du, Thorsten Joachims
2019	A*	`conf`	Intervention Harvesting for Context-Dependent Examination-Bias Estimation. SIGIR Zhichong Fang, Aman Agarwal, Thorsten Joachims
2019	A*	`conf`	Policy Learning for Fairness in Ranking. NeurIPS Ashudeep Singh, Thorsten Joachims
2019	J	`jnl`	Policy Learning for Fairness in Ranking. CoRR Ashudeep Singh, Thorsten Joachims
2019	A	`conf`	REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation. RecSys Thorsten Joachims, Maria Dimakopoulou, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile
2019	A	`conf`	Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. WSDM Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
2018	J	`jnl`	CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning. CoRR Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
2018	J	`jnl`	Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank. CoRR Aman Agarwal, Ivan Zaitsev, Thorsten Joachims
2018	J	`jnl`	Counterfactual Learning-to-Rank for Additive Metrics and Deep Models. CoRR Aman Agarwal, Ivan Zaitsev, Thorsten Joachims
2018	—	`conf`	Deep Learning from Logged Interventions. DLRS@RecSys Thorsten Joachims
2018	—	`conf`	Deep Learning with Logged Bandit Feedback. ICLR (Poster) Thorsten Joachims, Adith Swaminathan, Maarten de Rijke
2018	J	`jnl`	Estimating Position Bias without Intrusive Interventions. CoRR Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims
2018	J	`jnl`	Extreme Classification (Dagstuhl Seminar 18291). Dagstuhl Reports Samy Bengio, Krzysztof Dembczynski, Thorsten Joachims, Marius Kloft, Manik Varma
2018	A*	`conf`	Fairness of Exposure in Rankings. KDD Ashudeep Singh, Thorsten Joachims
2018	J	`jnl`	Fairness of Exposure in Rankings. CoRR Ashudeep Singh, Thorsten Joachims
2018	J	`jnl`	Improving Recommender Systems Beyond the Algorithm. CoRR Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
2018	J	`jnl`	Intervention Harvesting for Context-Dependent Examination-Bias Estimation. CoRR Zhichong Fang, Aman Agarwal, Thorsten Joachims
2018	A	`conf`	REVEAL 2018: offline evaluation for recommender systems. RecSys Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile
2018	A	`conf`	Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. WSDM Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
2018	A*	`conf`	Unbiased Learning-to-Rank with Biased Feedback. IJCAI Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
2017	J	`jnl`	A preference elicitation interface for collecting dense recommender datasets with rich user information. CoRR Pantelis P. Analytis, Tobias Schnabel, Stefan M. Herzog, Daniel Barkoczi, Thorsten Joachims
2017	J	`jnl`	Accurately Interpreting Clickthrough Data as Implicit Feedback. SIGIR Forum Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay
2017	A*	`conf`	Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. KDD Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
2017	J	`jnl`	Effective Evaluation using Logged Bandit Feedback from Multiple Loggers. CoRR Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
2017	A	`conf`	Ranking with Social Cues: Integrating Online Review Scores and Popularity Information. ICWSM Pantelis P. Analytis, Alexia Delfino, Juliane E. Kämmer, Mehdi Moussaïd, Thorsten Joachims
2017	J	`jnl`	Ranking with social cues: Integrating online review scores and popularity information. CoRR Pantelis P. Analytis, Alexia Delfino, Juliane E. Kämmer, Mehdi Moussaïd, Thorsten Joachims
2017	A	`conf`	Unbiased Learning-to-Rank with Biased Feedback. WSDM Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
2016	—	`conf`	Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015. Solving Large Scale Learning Tasks Thorsten Joachims, Karthik Raman
2016	A*	`conf`	Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. SIGIR Thorsten Joachims, Adith Swaminathan
2016	J	`jnl`	Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. CoRR Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke
2016	J	`jnl`	Latent Skill Embedding for Personalized Lesson Sequence Recommendation. CoRR Siddharth Reddy, Igor Labutov, Thorsten Joachims
2016	J	`jnl`	Learning Preferences for Manipulation Tasks from Online Coactive Feedback. CoRR Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena
2016	B	`conf`	Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation. L@S Siddharth Reddy, Igor Labutov, Thorsten Joachims
2016	A	`conf`	Modeling Intransitivity in Matchup and Comparison Data. WSDM Shuo Chen, Thorsten Joachims
2016	A*	`conf`	Predicting Matchups and Preferences in Context. KDD Shuo Chen, Thorsten Joachims
2016	A*	`conf`	Recommendations as Treatments: Debiasing Learning and Evaluation. ICML Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016	J	`jnl`	Recommendations as Treatments: Debiasing Learning and Evaluation. CoRR Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
2016	Misc	`conf`	Unbiased Comparative Evaluation of Ranking Functions. ICTIR Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims
2016	J	`jnl`	Unbiased Comparative Evaluation of Ranking Functions. CoRR Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims
2016	J	`jnl`	Unbiased Learning-to-Rank with Biased Feedback. CoRR Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
2016	A*	`conf`	Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
2016	J	`jnl`	Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. CoRR Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
2016	A*	`conf`	Using Shortlists to Support Decision Making and Improve Recommender System Performance. WWW Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
2015	J	`jnl`	Analysis of nutrition data by means of a matrix factorization method. Prog. Artif. Intell. Jorge Díez, Edna Gamboa, Teresita González de Cossío, Oscar Luaces, Thorsten Joachims, Antonio Bahamonde
2015	J	`jnl`	Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. Adith Swaminathan, Thorsten Joachims
2015	B	`conf`	Bayesian Ordinal Peer Grading. L@S Karthik Raman, Thorsten Joachims
2015	J	`jnl`	Coactive Learning. J. Artif. Intell. Res. Pannaga Shivaswamy, Thorsten Joachims
2015	—	`conf`	Counterfactual Risk Minimization. WWW (Companion Volume) Adith Swaminathan, Thorsten Joachims
2015	A*	`conf`	Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. ICML Adith Swaminathan, Thorsten Joachims
2015	J	`jnl`	Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. CoRR Adith Swaminathan, Thorsten Joachims
2015	A*	`conf`	Evaluation methods for unsupervised word embeddings. EMNLP Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims
2015	A	`conf`	Learning from User Interactions. WSDM Thorsten Joachims
2015	J	`jnl`	Learning preferences for manipulation tasks from online coactive feedback. Int. J. Robotics Res. Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena
2015	J	`jnl`	Mapping preferences into Euclidean space. Expert Syst. Appl. Oscar Luaces, Jorge Díez, Thorsten Joachims, Antonio Bahamonde
2015	A*	`ed.`	Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015 KDD Longbing Cao, Chengqi Zhang, Thorsten Joachims, Geoffrey I. Webb, Dragos D. Margineantu, Graham Williams
2015	—	`conf`	The Self-Normalized Estimator for Counterfactual Learning. NIPS Adith Swaminathan, Thorsten Joachims
2015	—	`conf`	Unbiased Ranking Evaluation on a Budget. WWW (Companion Volume) Tobias Schnabel, Adith Swaminathan, Thorsten Joachims
2015	J	`jnl`	Using Shortlists to Support Decision Making and Improve Recommender System Performance. CoRR Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims
2014	J	`jnl`	Behavior Informatics: A New Perspective. IEEE Intell. Syst. Longbing Cao, Thorsten Joachims, Can Wang, Éric Gaussier, Jinjiu Li, Yuming Ou, Dan Luo, Reza Zafarani, Huan Liu, Guandong Xu, Zhiang Wu, Gabriella Pasi, Ya Zhang, Xiaokang Yang, Hongyuan Zha, Edoardo Serra, V. S. Subrahmanian
2014	A*	`conf`	Invited Talk: Learning from Rational Behavior. EMNLP Thorsten Joachims
2014	J	`jnl`	Methods for Ordinal Peer Grading. CoRR Karthik Raman, Thorsten Joachims
2014	A*	`conf`	Methods for ordinal peer grading. KDD Karthik Raman, Thorsten Joachims
2014	A*	`conf`	Reducing Dueling Bandits to Cardinal Bandits. ICML Nir Ailon, Zohar Shay Karnin, Thorsten Joachims
2014	J	`jnl`	Reducing Dueling Bandits to Cardinal Bandits. CoRR Nir Ailon, Thorsten Joachims, Zohar Shay Karnin
2014	—	`conf`	Taste Space Versus the World: an Embedding Analysis of Listening Habits and Geography. ISMIR Joshua L. Moore, Thorsten Joachims, Douglas R. Turnbull
2014	—	`conf`	Using personalized radio to enhance local music discovery. CHI Extended Abstracts Douglas R. Turnbull, Justin A. Zupnick, Kristofer B. Stensland, Andrew R. Horwitz, Alexander J. Wolf, Alexander E. Spirgel, Stephen P. Meyerhofer, Thorsten Joachims
2014	A*	`conf`	Was this review helpful to you?: it depends! context and voting patterns in online content. WWW Ruben Sipos, Arpita Ghosh, Thorsten Joachims
2013	A*	`conf`	Beyond myopic inference in big data pipelines. KDD Karthik Raman, Adith Swaminathan, Johannes Gehrke, Thorsten Joachims
2013	J	`jnl`	Contextually guided semantic labeling and search for three-dimensional point clouds. Int. J. Robotics Res. Abhishek Anand, Hema Swetha Koppula, Thorsten Joachims, Ashutosh Saxena
2013	A	`conf`	Generating comparative summaries from reviews. CIKM Ruben Sipos, Thorsten Joachims
2013	—	`conf`	Learning Socially Optimal Information Systems from Egoistic Users. ECML/PKDD (2) Karthik Raman, Thorsten Joachims
2013	—	`conf`	Learning Trajectory Preferences for Manipulators via Iterative Improvement. NIPS Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
2013	J	`jnl`	Learning Trajectory Preferences for Manipulators via Iterative Improvement. CoRR Ashesh Jain, Thorsten Joachims, Ashutosh Saxena
2013	—	`ch.`	Maschinelles Lernen und Data Mining. Handbuch der Künstlichen Intelligenz Stefan Wrobel, Thorsten Joachims, Katharina Morik
2013	A*	`conf`	Multi-space probabilistic sequence modeling. KDD Shuo Chen, Jiexun Xu, Thorsten Joachims
2013	—	`conf`	Stable Coactive Learning via Perturbation. ICML (3) Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel
2013	A*	`conf`	Structured Learning of Sum-of-Submodular Higher Order Energy Functions. ICCV Alexander Fix, Thorsten Joachims, Sung Min Park, Ramin Zabih
2013	J	`jnl`	Structured learning of sum-of-submodular higher order energy functions. CoRR Alexander Fix, Thorsten Joachims, Sam Park, Ramin Zabih
2013	—	`conf`	Taste Over Time: The Temporal Dynamics of User Preferences. ISMIR Joshua L. Moore, Shuo Chen, Douglas R. Turnbull, Thorsten Joachims
2012	A	`conf`	Large-Margin Learning of Submodular Summarization Models. EACL Ruben Sipos, Pannaga Shivaswamy, Thorsten Joachims
2012	J	`jnl`	Large-scale validation and analysis of interleaved search evaluation. ACM Trans. Inf. Syst. Olivier Chapelle, Thorsten Joachims, Filip Radlinski, Yisong Yue
2012	—	`conf`	Learning to Embed Songs and Tags for Playlist Prediction. ISMIR Joshua L. Moore, Shuo Chen, Thorsten Joachims, Douglas R. Turnbull
2012	A	`conf`	Multi-armed Bandit Problems with History. AISTATS Pannagadatta K. Shivaswamy, Thorsten Joachims
2012	J	`jnl`	Online Structured Prediction via Coactive Learning CoRR Pannaga Shivaswamy, Thorsten Joachims
2012	A*	`conf`	Online Structured Prediction via Coactive Learning. ICML Pannaga Shivaswamy, Thorsten Joachims
2012	A*	`conf`	Online learning to diversify from implicit feedback. KDD Karthik Raman, Pannaga Shivaswamy, Thorsten Joachims
2012	A*	`conf`	Playlist prediction via metric embedding. KDD Shuo Chen, Joshua L. Moore, Douglas R. Turnbull, Thorsten Joachims
2012	A	`conf`	Temporal corpus summarization using submodular word coverage. CIKM Ruben Sipos, Adith Swaminathan, Pannaga Shivaswamy, Thorsten Joachims
2012	J	`jnl`	The K-armed dueling bandits problem. J. Comput. Syst. Sci. Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
2012	J	`jnl`	Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation CoRR Carmel Domshlak, Thorsten Joachims
2011	A*	`conf`	Beat the Mean Bandit. ICML Yisong Yue, Thorsten Joachims
2011	J	`jnl`	Contextually Guided Semantic Labeling and Search for 3D Point Clouds CoRR Abhishek Anand, Hema Swetha Koppula, Thorsten Joachims, Ashutosh Saxena
2011	A	`conf`	Dynamic ranked retrieval. WSDM Christina Brandt, Thorsten Joachims, Yisong Yue, Jacob Bank
2011	J	`jnl`	Enriching information retrieval. SIGIR Forum Paul N. Bennett, Khalid El-Arini, Thorsten Joachims, Krysta M. Svore
2011	J	`jnl`	Labeling 3D scenes for Personal Assistant Robots CoRR Hema Swetha Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
2011	J	`jnl`	Large-Margin Learning of Submodular Summarization Methods CoRR Ruben Sipos, Pannaga Shivaswamy, Thorsten Joachims
2011	J	`jnl`	Online Learning with Preference Feedback CoRR Pannagadatta K. Shivaswamy, Thorsten Joachims
2011	—	`conf`	Semantic Labeling of 3D Point Clouds for Indoor Scenes. NIPS Hema Swetha Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
2011	J	`jnl`	Structured Learning of Two-Level Dynamic Rankings CoRR Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy
2011	A	`conf`	Structured learning of two-level dynamic rankings. CIKM Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy
2011	A	`conf`	The Value of User Feedback. ECIR Thorsten Joachims
2010	—	`ch.`	Evaluating Search Engine Relevance with Click-Based Metrics. Preference Learning Filip Radlinski, Madhu Kurup, Thorsten Joachims
2010	—	`conf`	Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes. ECML/PKDD (3) Zhao Xu, Kristian Kersting, Thorsten Joachims
2010	J	`jnl`	Introduction to special issue on learning to rank for information retrieval. Inf. Retr. Tie-Yan Liu, Thorsten Joachims, Hang Li, ChengXiang Zhai
2010	A*	`conf`	Learning more powerful test statistics for click-based retrieval evaluation. SIGIR Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, Thorsten Joachims
2010	A*	`ed.`	Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel ICML Johannes Fürnkranz, Thorsten Joachims
2009	J	`jnl`	Cutting-plane training of structural SVMs. Mach. Learn. Thorsten Joachims, Thomas Finley, Chun-Nam John Yu
2009	—	`conf`	Identifying the Original Contribution of a Document via Language Modeling. ECML/PKDD (2) Benyah Shaparenko, Thorsten Joachims
2009	A*	`conf`	Identifying the original contribution of a document via language modeling. SIGIR Benyah Shaparenko, Thorsten Joachims
2009	A*	`conf`	Interactively optimizing information retrieval systems as a dueling bandits problem. ICML Yisong Yue, Thorsten Joachims
2009	A*	`conf`	Learning structural SVMs with latent variables. ICML Chun-Nam John Yu, Thorsten Joachims
2009	J	`jnl`	Predicting structured objects with support vector machines. Commun. ACM Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam John Yu
2009	J	`jnl`	Redundancy, diversity and interdependent document relevance. SIGIR Forum Filip Radlinski, Paul N. Bennett, Ben Carterette, Thorsten Joachims
2009	—	`conf`	Sparse Kernel SVMs via Cutting-Plane Training. ECML/PKDD (1) Thorsten Joachims, Chun-Nam John Yu
2009	J	`jnl`	Sparse kernel SVMs via cutting-plane training. Mach. Learn. Thorsten Joachims, Chun-Nam John Yu
2009	A*	`conf`	The K-armed Dueling Bandits Problem. COLT Yisong Yue, Josef Broder, Robert Kleinberg, Thorsten Joachims
2008	J	`jnl`	Beyond binary relevance: preferences, diversity, and set-level judgments. SIGIR Forum Paul N. Bennett, Ben Carterette, Olivier Chapelle, Thorsten Joachims
2008	J	`jnl`	Eye tracking and online search: Lessons learned and challenges ahead. J. Assoc. Inf. Sci. Technol. Lori Lorigo, Maya Haridasan, Hrönn Brynjarsdóttir, Ling Xia, Thorsten Joachims, Geri Gay, Laura A. Granka, Fabio Pellacini, Bing Pan
2008	A	`conf`	How does clickthrough data reflect retrieval quality? CIKM Filip Radlinski, Madhu Kurup, Thorsten Joachims
2008	—	`conf`	Invited speaker: Structured Output Prediction with Structural SVMs. MLG@ICML Thorsten Joachims
2008	A*	`conf`	Learning diverse rankings with multi-armed bandits. ICML Filip Radlinski, Robert Kleinberg, Thorsten Joachims
2008	A*	`conf`	Predicting diverse subsets using structural SVMs. ICML Yisong Yue, Thorsten Joachims
2008	J	`jnl`	Support Vector Training of Protein Alignment Models. J. Comput. Biol. Chun-Nam John Yu, Thorsten Joachims, Ron Elber, Jaroslaw Pillardy
2008	J	`jnl`	Text categorization. Scholarpedia Yiming Yang, Thorsten Joachims
2008	A*	`conf`	Training structural SVMs when exact inference is intractable. ICML Thomas Finley, Thorsten Joachims
2008	A*	`conf`	Training structural svms with kernels using sampled cuts. KDD Chun-Nam John Yu, Thorsten Joachims
2007	A*	`conf`	A support vector method for optimizing average precision. SIGIR Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims
2007	A*	`conf`	Active exploration for learning rankings from clickthrough data. KDD Filip Radlinski, Thorsten Joachims
2007	J	`jnl`	Efficient and non-parametric reasoning over user preferences. User Model. User Adapt. Interact. Carmel Domshlak, Thorsten Joachims
2007	J	`jnl`	Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search. ACM Trans. Inf. Syst. Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, Geri Gay
2007	J	`jnl`	In Google We Trust: Users' Decisions on Rank, Position, and Relevance. J. Comput. Mediat. Commun. Bing Pan, Helene Hembrooke, Thorsten Joachims, Lori Lorigo, Geri Gay, Laura A. Granka
2007	A*	`conf`	Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases. KDD Benyah Shaparenko, Thorsten Joachims
2007	J	`jnl`	Learning to rank for information retrieval (LR4IR 2007). SIGIR Forum Thorsten Joachims, Hang Li, Tie-Yan Liu, ChengXiang Zhai
2007	J	`jnl`	Recommending Related Papers Based on Digital Library Access Records CoRR Stefan Pohl, Filip Radlinski, Thorsten Joachims
2007	—	`conf`	Recommending related papers based on digital library access records. JCDL Stefan Pohl, Filip Radlinski, Thorsten Joachims
2007	J	`jnl`	Search Engines that Learn from Implicit Feedback. Computer Thorsten Joachims, Filip Radlinski
2007	B	`conf`	Support Vector Training of Protein Alignment Models. RECOMB Chun-Nam John Yu, Thorsten Joachims, Ron Elber, Jaroslaw Pillardy
2006	J	`jnl`	Evaluating the Robustness of Learning from Implicit Feedback CoRR Filip Radlinski, Thorsten Joachims
2006	J	`jnl`	Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs CoRR Filip Radlinski, Thorsten Joachims
2006	A*	`conf`	Minimally Invasive Randomization fro Collecting Unbiased Preferences from Clickthrough Logs. AAAI Filip Radlinski, Thorsten Joachims
2006	J	`jnl`	Query Chains: Learning to Rank from Implicit Feedback CoRR Filip Radlinski, Thorsten Joachims
2006	—	`conf`	Structured Output Prediction with Support Vector Machines. SSPR/SPR Thorsten Joachims
2006	J	`jnl`	The influence of task and gender on search and evaluation behavior using Google. Inf. Process. Manag. Lori Lorigo, Bing Pan, Helene Hembrooke, Thorsten Joachims, Laura A. Granka, Geri Gay
2006	A*	`conf`	Training linear SVMs in linear time. KDD Thorsten Joachims
2006	—	`ch.`	Transductive Support Vector Machines. Semi-Supervised Learning Thorsten Joachims
2005	A*	`conf`	A support vector method for multivariate performance measures. ICML Thorsten Joachims
2005	A*	`conf`	Accurately interpreting clickthrough data as implicit feedback. SIGIR Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay
2005	A*	`conf`	Error bounds for correlation clustering. ICML Thorsten Joachims, John E. Hopcroft
2005	J	`jnl`	Large Margin Methods for Structured and Interdependent Output Variables. J. Mach. Learn. Res. Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun
2005	A*	`conf`	Query chains: learning to rank from implicit feedback. KDD Filip Radlinski, Thorsten Joachims
2005	A*	`conf`	Supervised clustering with support vector machines. ICML Thomas Finley, Thorsten Joachims
2005	A	`conf`	Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation. UAI Carmel Domshlak, Thorsten Joachims
2004	A*	`conf`	Eye-tracking analysis of user behavior in WWW search. SIGIR Laura A. Granka, Thorsten Joachims, Geri Gay
2004	J	`jnl`	KDD-Cup 2004: results and analysis. SIGKDD Explor. Rich Caruana, Thorsten Joachims, Lars Backstrom
2004	A*	`conf`	Support vector machine learning for interdependent and structured output spaces. ICML Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun
2003	—	`ch.`	Evaluating Retrieval Performance Using Clickthrough Data. Text Mining Thorsten Joachims
2003	—	`conf`	Learning a Distance Metric from Relative Comparisons. NIPS Matthew Schultz, Thorsten Joachims
2003	J	`jnl`	Mapping Subsets of Scholarly Information CoRR Paul Ginsparg, Paul Houle, Thorsten Joachims, Jae-Hoon Sul
2003	—	`ch.`	Maschinelles Lernen und Data Mining. Handbuch der Künstlichen Intelligenz Stefan Wrobel, Katharina Morik, Thorsten Joachims
2003	J	`jnl`	SIGIR 2003 workshop report: implicit measures of user interests and preferences. SIGIR Forum Susan T. Dumais, Thorsten Joachims, Krishna Bharat, Andreas S. Weigend
2003	A*	`conf`	Transductive Learning via Spectral Graph Partitioning. ICML Thorsten Joachims
2002	—	`conf`	Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis. COMPSTAT Andreas Christmann, Paul Fischer, Thorsten Joachims
2002	J	`jnl`	Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications. Comput. Stat. Andreas Christmann, Paul Fischer, Thorsten Joachims
2002	J	`jnl`	Guest Editors' Introduction to the Special Issue on Automated Text Categorization. J. Intell. Inf. Syst. Thorsten Joachims, Fabrizio Sebastiani
2002	—	`book`	Learning to classify text using support vector machines - methods, theory and algorithms. Thorsten Joachims
2002	A*	`conf`	Optimizing search engines using clickthrough data. KDD Thorsten Joachims
2002	J	`jnl`	Text-Mining - Serviceteil. Künstliche Intell. Thorsten Joachims, Edda Leopold
2002	A	`conf`	The enigmatics of affect. Symposium on Designing Interactive Systems Phoebe Sengers, Rainer Liesendahi, Werner Magar, Christoph Seibert, Boris Müller, Thorsten Joachims, Weidong Geng, Pia Mårtensson, Kristina Höök
2001	A*	`conf`	A Statistical Learning Model of Text Classification for Support Vector Machines. SIGIR Thorsten Joachims
2001	A*	`conf`	Composite Kernels for Hypertext Categorisation. ICML Thorsten Joachims, Nello Cristianini, John Shawe-Taylor
2001	J	`jnl`	The Maximum-Margin Approach to Learning Text Classifiers. Künstliche Intell. Thorsten Joachims
2001	—	`ch.`	The maximum-margin approach to learning text classifiers: methods theory, and algorithms. Ausgezeichnete Informatikdissertationen Thorsten Joachims
2000	A*	`conf`	Detecting Concept Drift with Support Vector Machines. ICML Ralf Klinkenberg, Thorsten Joachims
2000	J	`jnl`	Erratum to "Knowledge discovery and knowledge validation in intensive care". Artif. Intell. Medicine Katharina Morik, Michael Imhoff, Peter Brockhausen, Thorsten Joachims, Ursula Gather
2000	A*	`conf`	Estimating the Generalization Performance of an SVM Efficiently. ICML Thorsten Joachims
2000	J	`jnl`	Knowledge discovery and knowledge validation in intensive care. Artif. Intell. Medicine Katharina Morik, Michael Imhoff, Peter Brockhausen, Thorsten Joachims, Ursula Gather
1999	A*	`conf`	Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. ICML Katharina Morik, Peter Brockhausen, Thorsten Joachims
1999	A*	`conf`	Expected Error Analysis for Model Selection. ICML Tobias Scheffer, Thorsten Joachims
1999	J	`jnl`	Support Vector Machines (Aktuelles Schlagwort). Künstliche Intell. Thorsten Joachims
1999	A*	`conf`	Transductive Inference for Text Classification using Support Vector Machines. ICML Thorsten Joachims
1998	J	`jnl`	Browsing-Assistenten, Tour Guides und adaptive WWW-Server. Künstliche Intell. Thorsten Joachims, Dunja Mladenic
1998	—	`conf`	Estimating the Expected Error of Empirical Minimizers for Model Selection. AAAI/IAAI Tobias Scheffer, Thorsten Joachims
1998	—	`conf`	Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML Thorsten Joachims
1997	A*	`conf`	A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. ICML Thorsten Joachims
1997	—	`conf`	Web Watcher: A Tour Guide for the World Wide Web. IJCAI (1) Thorsten Joachims, Dayne Freitag, Tom M. Mitchell