| Multileave gradient descent for fast online learning to rank A Schuth, H Oosterhuis, S Whiteson, M de Rijke Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016 | 53 | 2016 |
| To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions R Jagerman, H Oosterhuis, M de Rijke Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 24 | 2019 |
| Probabilistic multileave for online retrieval evaluation A Schuth, RJ Bruintjes, F Buüttner, J van Doorn, C Groenland, ... Proceedings of the 38th International ACM SIGIR Conference on Research and …, 2015 | 24 | 2015 |
| Differentiable unbiased online learning to rank H Oosterhuis, M de Rijke Proceedings of the 27th ACM International Conference on Information and …, 2018 | 23 | 2018 |
| Probabilistic multileave gradient descent H Oosterhuis, A Schuth, M de Rijke European Conference on Information Retrieval, 661-668, 2016 | 21 | 2016 |
| Ranking for Relevance and Display Preferences in Complex Presentation Layouts H Oosterhuis, M de Rijke SIGIR 2018: 41st international ACM SIGIR conference on Research and …, 2018 | 17 | 2018 |
| Sensitive and Scalable Online Evaluation with Theoretical Guarantees H Oosterhuis, M de Rijke CIKM '17 ACM Conference on Information and Knowledge Management, 77-86, 2017 | 16 | 2017 |
| Balancing Speed and Quality in Online Learning to Rank for Information Retrieval H Oosterhuis, M de Rijke CIKM '17 ACM Conference on Information and Knowledge Management, 277-286, 2017 | 14 | 2017 |
| The Potential of Learned Index Structures for Index Compression H Oosterhuis, JS Culpepper, M de Rijke Australasian Document Computing Symposium (ADCS) 23, 2018 | 12 | 2018 |
| Semantic video trailers H Oosterhuis, S Ravi, M Bendersky ICML 2016 Workshop on Multi-View Representation Learning, 2016 | 9 | 2016 |
| Optimizing Ranking Models in an Online Setting H Oosterhuis, M de Rijke European Conference on Information Retrieval, 382-396, 2019 | 8 | 2019 |
| Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles A Lucic, H Oosterhuis, H Haned, M de Rijke arXiv preprint arXiv:1911.12199, 2019 | 6 | 2019 |
| Policy-Aware Unbiased Learning to Rank for Top-k Rankings H Oosterhuis, M de Rijke arXiv preprint arXiv:2005.09035, 2020 | 5 | 2020 |
| Unbiased Learning to Rank: Counterfactual and Online Approaches H Oosterhuis, R Jagerman, M de Rijke Companion Proceedings of the Web Conference 2020, 299-300, 2020 | 4 | 2020 |
| Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning C Lucchese, FM Nardini, RK Pasumarthi, S Bruch, M Bendersky, X Wang, ... Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 3 | 2019 |
| Query-level Ranker Specialization R Jagerman, H Oosterhuis, M de Rijke 1st International Workshop on LEARning Next gEneration Rankers (LEARNER), 2017 | 3 | 2017 |
| When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank A Vardasbi, H Oosterhuis, M de Rijke Proceedings of the 29th ACM International Conference on Information …, 2020 | 2 | 2020 |
| Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking H Oosterhuis, M de Rijke Proceedings of the 2020 ACM SIGIR on International Conference on Theory of …, 2020 | 2 | 2020 |
| Unifying Online and Counterfactual Learning to Rank H Oosterhuis, M de Rijke arXiv preprint arXiv:2012.04426, 2020 | 1 | 2020 |
| Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems J Huang, H Oosterhuis, M de Rijke, H van Hoof Fourteenth ACM Conference on Recommender Systems, 190-199, 2020 | 1 | 2020 |
Maarten de RijkeUniversity of Amsterdam - Ahold Delhaize - ICAIBestätigte E-Mail-Adresse bei uva.nl
