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Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity.. SIGIR, стр. 2266-2271. ACM, (2022)Session details: Session 4B - Semantic Retrieval.. ICTIR, ACM, (2021)Query-Level Ranker Specialization., , и . LEARNER@ICTIR, том 2007 из CEUR Workshop Proceedings, CEUR-WS.org, (2017)It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic., , и . WSDM, стр. 381-389. ACM, (2022)The Potential of Learned Index Structures for Index Compression., , и . ADCS, стр. 7:1-7:4. ACM, (2018)Probabilistic Multileave for Online Retrieval Evaluation., , , , , , , , , и 2 other автор(ы). SIGIR, стр. 955-958. ACM, (2015)To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions., , и . SIGIR, стр. 15-24. ACM, (2019)Probabilistic Multileave Gradient Descent., , и . ECIR, том 9626 из Lecture Notes in Computer Science, стр. 661-668. Springer, (2016)Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems., , , и . RecSys, стр. 190-199. ACM, (2020)A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization., , и . CoRR, (2024)