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ORCIDThorsten Joachims
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- affiliation: Cornell University, Ithaca, USA
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2020 – today
- 2024
[j40]A. J. Alvero, Jinsook Lee, Alejandra Regla-Vargas, René F. Kizilcec, Thorsten Joachims, Anthony Lising Antonio:
Large language models, social demography, and hegemony: comparing authorship in human and synthetic text. J. Big Data 11(1): 138 (2024)- [c137]Aaron David Tucker, Kianté Brantley, Adam Cahall, Thorsten Joachims:
Coactive Learning for Large Language Models using Implicit User Feedback. ICML 2024 - [c136]Wentao Guo
, Andrew Wang, Bradon Thymes, Thorsten Joachims:
Ranking with Slot Constraints. KDD 2024: 956-967 - [c135]Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Jan Malte Lichtenberg, Yannik Stein, Thorsten Joachims:
Counterfactual Ranking Evaluation with Flexible Click Models. SIGIR 2024: 1200-1210 - [c134]Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims:
Ranking with Long-Term Constraints. WSDM 2024: 47-56 - [e3]Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, Ben London:
Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024. ACM 2024, ISBN 979-8-4007-0505-2 [contents] - [i68]Yuta Saito, Jihan Yao, Thorsten Joachims:
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition. CoRR abs/2402.06151 (2024) - [i67]Zhaolin Gao, Kianté Brantley, Thorsten Joachims:
Reviewer2: Optimizing Review Generation Through Prompt Generation. CoRR abs/2402.10886 (2024) - [i66]Joyce Zhou, Yijia Dai, Thorsten Joachims:
Language-Based User Profiles for Recommendation. CoRR abs/2402.15623 (2024) - [i65]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 abs/2404.16767 (2024) - [i64]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 abs/2407.11199 (2024) - 2023
- [c133]Ben London, Levi Lu, Ted Sandler, Thorsten Joachims:
Boosted Off-Policy Learning. AISTATS 2023: 5614-5640 - [c132]Joyce Zhou, Thorsten Joachims:
How to Explain and Justify Almost Any Decision: Potential Pitfalls for Accountability in AI Decision-Making. FAccT 2023: 12-21 - [c131]Yuta Saito, Qingyang Ren, Thorsten Joachims:
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. ICML 2023: 29734-29759 - [c130]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: 195-203 - [c129]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: 55-61 - [c128]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: 1200-1203 - [c127]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: 1223-1226 - [c126]Aaron David Tucker, Caleb Biddulph, Claire Wang, Thorsten Joachims:
Bandits with costly reward observations. UAI 2023: 2147-2156 - [c125]Lequn Wang, Thorsten Joachims:
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. WSDM 2023: 940-948 - [c124]Aaron David Tucker, Thorsten Joachims:
Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. WSDM 2023: 967-975 - [i63]Hansol Lee, René F. Kizilcec, Thorsten Joachims:
Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College Admissions. CoRR abs/2302.03610 (2023) - [i62]Yuta Saito, Qingyang Ren, Thorsten Joachims:
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. CoRR abs/2305.08062 (2023) - [i61]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 abs/2306.17575 (2023) - [i60]Kianté Brantley, Zhichong Fang, Sarah Dean, Thorsten Joachims:
Ranking with Long-Term Constraints. CoRR abs/2307.04923 (2023) - [i59]Richa Rastogi, Thorsten Joachims:
Fair Ranking under Disparate Uncertainty. CoRR abs/2309.01610 (2023) - [i58]Joyce Zhou, Thorsten Joachims:
GPT as a Baseline for Recommendation Explanation Texts. CoRR abs/2309.08817 (2023) - [i57]Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims:
Policy-Gradient Training of Language Models for Ranking. CoRR abs/2310.04407 (2023) - [i56]Wentao Guo, Andrew Wang, Bradon Thymes, Thorsten Joachims:
Ranking with Slot Constraints. CoRR abs/2310.17870 (2023) - [i55]Matej Jakimov, Alexander Buchholz, Yannik Stein, Thorsten Joachims:
Unbiased Offline Evaluation for Learning to Rank with Business Rules. CoRR abs/2311.01828 (2023) - 2022
- [c123]Yuta Saito, Thorsten Joachims:
Off-Policy Evaluation for Large Action Spaces via Embeddings. ICML 2022: 19089-19122 - [c122]Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez:
Improving Screening Processes via Calibrated Subset Selection. ICML 2022: 22702-22726 - [c121]Yuta Saito, Thorsten Joachims:
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. KDD 2022: 1514-1524 - [c120]Yuta Saito, Thorsten Joachims:
Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances. KDD 2022: 4824-4825 - [c119]Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile:
CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys 2022: 654-657 - [c118]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. SIGIR 2022: 791-802 - [c117]Yi Su, Magd Bayoumi, Thorsten Joachims:
Optimizing Rankings for Recommendation in Matching Markets. WWW 2022: 328-338 - [i54]Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez:
Improving Screening Processes via Calibrated Subset Selection. CoRR abs/2202.01147 (2022) - [i53]Aaron David Tucker, Thorsten Joachims:
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. CoRR abs/2202.01721 (2022) - [i52]Yuta Saito, Thorsten Joachims:
Off-Policy Evaluation for Large Action Spaces via Embeddings. CoRR abs/2202.06317 (2022) - [i51]Adam Block, Rahul Kidambi, Daniel N. Hill, Thorsten Joachims, Inderjit S. Dhillon:
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. CoRR abs/2204.10936 (2022) - [i50]Lequn Wang, Thorsten Joachims:
Fairness in the First Stage of Two-Stage Recommender Systems. CoRR abs/2205.15436 (2022) - [i49]Yuta Saito, Thorsten Joachims:
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. CoRR abs/2206.07247 (2022) - [i48]Ben London, Levi Lu, Ted Sandler, Thorsten Joachims:
Boosted Off-Policy Learning. CoRR abs/2208.01148 (2022) - [i47]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 abs/2210.09512 (2022) - 2021
- [j39]Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang:
Recommendations as Treatments. AI Mag. 42(3): 19-30 (2021) - [c116]Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims:
Fairness of Exposure in Stochastic Bandits. ICML 2021: 10686-10696 - [c115]Thorsten Joachims:
Fairness and Control of Exposure in Two-sided Markets. ICTIR 2021: 1 - [c114]Lequn Wang, Thorsten Joachims:
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets. ICTIR 2021: 23-41 - [c113]Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims:
Controlling Fairness and Bias in Dynamic Learning-to-Rank (Extended Abstract). IJCAI 2021: 4804-4808 - [c112]Ashudeep Singh, David Kempe, Thorsten Joachims:
Fairness in Ranking under Uncertainty. NeurIPS 2021: 11896-11908 - [c111]Yuta Saito, Thorsten Joachims:
Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances. RecSys 2021: 828-830 - [c110]Himank Yadav, Zhengxiao Du, Thorsten Joachims:
Policy-Gradient Training of Fair and Unbiased Ranking Functions. SIGIR 2021: 1044-1053 - [i46]Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims:
Fairness of Exposure in Stochastic Bandits. CoRR abs/2103.02735 (2021) - [i45]Yi Su, Magd Bayoumi, Thorsten Joachims:
Optimizing Rankings for Recommendation in Matching Markets. CoRR abs/2106.01941 (2021) - [i44]Ashudeep Singh, David Kempe, Thorsten Joachims:
Fairness in Ranking under Uncertainty. CoRR abs/2107.06720 (2021) - 2020
- [j38]Thorsten Joachims:
An Interview with Dr. Thorsten Joachims, Winner of ACM SIGKDD 2020 Innovation Award. SIGKDD Explor. 22(2): 2-5 (2020) - [c109]Derek Cheng, Thorsten Joachims, Douglas R. Turnbull:
Exploring Acoustic Similarity for Novel Music Recommendation. ISMIR 2020: 583-589 - [c108]Noveen Sachdeva, Yi Su, Thorsten Joachims:
Off-policy Bandits with Deficient Support. KDD 2020: 965-975 - [c107]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS 2020 - [c106]Thorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan:
REVEAL 2020: Bandit and Reinforcement Learning from User Interactions. RecSys 2020: 628-629 - [c105]Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims:
Controlling Fairness and Bias in Dynamic Learning-to-Rank. SIGIR 2020: 429-438 - [c104]Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims:
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. SIGIR 2020: 991-1000 - [i43]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL : Model-Based Offline Reinforcement Learning. CoRR abs/2005.05951 (2020) - [i42]Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims:
Controlling Fairness and Bias in Dynamic Learning-to-Rank. CoRR abs/2005.14713 (2020) - [i41]Noveen Sachdeva, Yi Su, Thorsten Joachims:
Off-policy Bandits with Deficient Support. CoRR abs/2006.09438 (2020) - [i40]Joseph Cleveland, Derek Cheng, Michael Zhou, Thorsten Joachims, Douglas R. Turnbull:
Content-based Music Similarity with Triplet Networks. CoRR abs/2008.04938 (2020) - [i39]Lequn Wang, Thorsten Joachims:
Fairness and Diversity for Rankings in Two-Sided Markets. CoRR abs/2010.01470 (2020)
2010 – 2019
- 2019
- [c103]Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims:
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. ICML 2019: 6005-6014 - [c102]Ashudeep Singh, Thorsten Joachims:
Policy Learning for Fairness in Ranking. NeurIPS 2019: 5427-5437 - [c101]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: 568-569 - [c100]Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims:
A General Framework for Counterfactual Learning-to-Rank. SIGIR 2019: 5-14 - [c99]Zhichong Fang, Aman Agarwal, Thorsten Joachims:
Intervention Harvesting for Context-Dependent Examination-Bias Estimation. SIGIR 2019: 825-834 - [c98]Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims:
Estimating Position Bias without Intrusive Interventions. WSDM 2019: 474-482 - [c97]Tobias Schnabel, Paul N. Bennett, Thorsten Joachims:
Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. WSDM 2019: 546-554 - [i38]Ashudeep Singh, Thorsten Joachims:
Policy Learning for Fairness in Ranking. CoRR abs/1902.04056 (2019) - [i37]Himank Yadav, Zhengxiao Du, Thorsten Joachims:
Fair Learning-to-Rank from Implicit Feedback. CoRR abs/1911.08054 (2019) - 2018
- [c96]Thorsten Joachims, Adith Swaminathan, Maarten de Rijke:
Deep Learning with Logged Bandit Feedback. ICLR (Poster) 2018 - [c95]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. IJCAI 2018: 5284-5288 - [c94]Ashudeep Singh, Thorsten Joachims:
Fairness of Exposure in Rankings. KDD 2018: 2219-2228 - [c93]Thorsten Joachims:
Deep Learning from Logged Interventions. DLRS@RecSys 2018: 1 - [c92]Thorsten Joachims, Adith Swaminathan, Yves Raimond, Olivier Koch, Flavian Vasile:
REVEAL 2018: offline evaluation for recommender systems. RecSys 2018: 514-515 - [c91]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: 513-521 - [i36]Ashudeep Singh, Thorsten Joachims:
Fairness of Exposure in Rankings. CoRR abs/1802.07281 (2018) - [i35]Tobias Schnabel, Paul N. Bennett, Thorsten Joachims:
Improving Recommender Systems Beyond the Algorithm. CoRR abs/1802.07578 (2018) - [i34]Aman Agarwal, Ivan Zaitsev, Thorsten Joachims:
Counterfactual Learning-to-Rank for Additive Metrics and Deep Models. CoRR abs/1805.00065 (2018) - [i33]Aman Agarwal, Ivan Zaitsev, Thorsten Joachims:
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank. CoRR abs/1806.03555 (2018) - [i32]Zhichong Fang, Aman Agarwal, Thorsten Joachims:
Intervention Harvesting for Context-Dependent Examination-Bias Estimation. CoRR abs/1811.01802 (2018) - [i31]Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims:
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning. CoRR abs/1811.02672 (2018) - [i30]Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, Thorsten Joachims:
Estimating Position Bias without Intrusive Interventions. CoRR abs/1812.05161 (2018) - [i29]Samy Bengio, Krzysztof Dembczynski, Thorsten Joachims, Marius Kloft, Manik Varma:
Extreme Classification (Dagstuhl Seminar 18291). Dagstuhl Reports 8(7): 62-80 (2018) - 2017
- [j37]Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay:
Accurately Interpreting Clickthrough Data as Implicit Feedback. SIGIR Forum 51(1): 4-11 (2017) - [c90]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: 468-471 - [c89]Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims:
Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. KDD 2017: 687-696 - [c88]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. WSDM 2017: 781-789 - [i28]Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims:
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers. CoRR abs/1703.06180 (2017) - [i27]Pantelis P. Analytis, Alexia Delfino, Juliane E. Kämmer, Mehdi Moussaïd, Thorsten Joachims:
Ranking with social cues: Integrating online review scores and popularity information. CoRR abs/1704.01213 (2017) - [i26]Pantelis P. Analytis, Tobias Schnabel, Stefan M. Herzog, Daniel Barkoczi, Thorsten Joachims:
A preference elicitation interface for collecting dense recommender datasets with rich user information. CoRR abs/1706.08184 (2017) - 2016
- [c87]Thorsten Joachims, Karthik Raman:
Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015. Solving Large Scale Learning Tasks 2016: 286-299 - [c86]Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims:
Recommendations as Treatments: Debiasing Learning and Evaluation. ICML 2016: 1670-1679 - [c85]Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims:
Unbiased Comparative Evaluation of Ranking Functions. ICTIR 2016: 109-118 - [c84]Shuo Chen, Thorsten Joachims:
Predicting Matchups and Preferences in Context. KDD 2016: 775-784 - [c83]Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims:
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. KDD 2016: 1815-1824 - [c82]Siddharth Reddy, Igor Labutov, Thorsten Joachims:
Learning Student and Content Embeddings for Personalized Lesson Sequence Recommendation. L@S 2016: 93-96 - [c81]Thorsten Joachims, Adith Swaminathan:
Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. SIGIR 2016: 1199-1201 - [c80]Shuo Chen, Thorsten Joachims:
Modeling Intransitivity in Matchup and Comparison Data. WSDM 2016: 227-236 - [c79]Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims:
Using Shortlists to Support Decision Making and Improve Recommender System Performance. WWW 2016: 987-997 - [i25]Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena:
Learning Preferences for Manipulation Tasks from Online Coactive Feedback. CoRR abs/1601.00741 (2016) - [i24]Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims:
Recommendations as Treatments: Debiasing Learning and Evaluation. CoRR abs/1602.05352 (2016) - [i23]Siddharth Reddy, Igor Labutov, Thorsten Joachims:
Latent Skill Embedding for Personalized Lesson Sequence Recommendation. CoRR abs/1602.07029 (2016) - [i22]Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims:
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition. CoRR abs/1602.07032 (2016) - [i21]Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims:
Unbiased Comparative Evaluation of Ranking Functions. CoRR abs/1604.07209 (2016) - [i20]Thorsten Joachims, Adith Swaminathan, Tobias Schnabel:
Unbiased Learning-to-Rank with Biased Feedback. CoRR abs/1608.04468 (2016) - [i19]Damien Lefortier, Adith Swaminathan, Xiaotao Gu, Thorsten Joachims, Maarten de Rijke:
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed. CoRR abs/1612.00367 (2016) - 2015
- [j36]Oscar Luaces, Jorge Díez, Thorsten Joachims, Antonio Bahamonde:
Mapping preferences into Euclidean space. Expert Syst. Appl. 42(22): 8588-8596 (2015) - [j35]Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena:
Learning preferences for manipulation tasks from online coactive feedback. Int. J. Robotics Res. 34(10): 1296-1313 (2015) - [j34]Pannaga Shivaswamy, Thorsten Joachims:
Coactive Learning. J. Artif. Intell. Res. 53: 1-40 (2015) - [j33]Adith Swaminathan, Thorsten Joachims:
Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16: 1731-1755 (2015) - [j32]Jorge Díez, Edna Gamboa, Teresita González de Cossío, Oscar Luaces, Thorsten Joachims, Antonio Bahamonde:
Analysis of nutrition data by means of a matrix factorization method. Prog. Artif. Intell. 3(3-4): 119-127 (2015) - [c78]Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims:
Evaluation methods for unsupervised word embeddings. EMNLP 2015: 298-307 - [c77]Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. ICML 2015: 814-823 - [c76]Karthik Raman, Thorsten Joachims:
Bayesian Ordinal Peer Grading. L@S 2015: 149-156 - [c75]Adith Swaminathan, Thorsten Joachims:
The Self-Normalized Estimator for Counterfactual Learning. NIPS 2015: 3231-3239 - [c74]Thorsten Joachims:
Learning from User Interactions. WSDM 2015: 137-138 - [c73]Tobias Schnabel, Adith Swaminathan, Thorsten Joachims:
Unbiased Ranking Evaluation on a Budget. WWW (Companion Volume) 2015: 935-937 - [c72]Adith Swaminathan, Thorsten Joachims:
Counterfactual Risk Minimization. WWW (Companion Volume) 2015: 939-941 - [e2]