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Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity.. SIGIR, page 2266-2271. ACM, (2022)It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic., , and . WSDM, page 381-389. ACM, (2022)Session details: Session 4B - Semantic Retrieval.. ICTIR, ACM, (2021)Query-Level Ranker Specialization., , and . LEARNER@ICTIR, volume 2007 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)The Potential of Learned Index Structures for Index Compression., , and . ADCS, page 7:1-7:4. ACM, (2018)Probabilistic Multileave for Online Retrieval Evaluation., , , , , , , , , and 2 other author(s). SIGIR, page 955-958. ACM, (2015)To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions., , and . SIGIR, page 15-24. ACM, (2019)Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems., , , and . RecSys, page 190-199. ACM, (2020)Probabilistic Multileave Gradient Descent., , and . ECIR, volume 9626 of Lecture Notes in Computer Science, page 661-668. Springer, (2016)Balancing Speed and Quality in Online Learning to Rank for Information Retrieval., and . CIKM, page 277-286. ACM, (2017)