| 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 | 77 | 2016 |
| Differentiable unbiased online learning to rank H Oosterhuis, M de Rijke Proceedings of the 27th ACM International Conference on Information and …, 2018 | 54 | 2018 |
| 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 | 49 | 2019 |
| 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 | 38 | 2020 |
| 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 | 32 | 2015 |
| Probabilistic multileave gradient descent H Oosterhuis, A Schuth, M Rijke European Conference on Information Retrieval, 661-668, 2016 | 27 | 2016 |
| Policy-aware unbiased learning to rank for top-k rankings H Oosterhuis, M de Rijke Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020 | 25 | 2020 |
| FOCUS: Flexible optimizable counterfactual explanations for tree ensembles A Lucic, H Oosterhuis, H Haned, M de Rijke arXiv preprint arXiv:1911.12199, 2020 | 25 | 2020 |
| The Potential of Learned Index Structures for Index Compression H Oosterhuis, JS Culpepper, M de Rijke Australasian Document Computing Symposium (ADCS) 23, 2018 | 25 | 2018 |
| 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 | 23 | 2018 |
| 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 | 20 | 2017 |
| Unifying online and counterfactual learning to rank: A novel counterfactual estimator that effectively utilizes online interventions H Oosterhuis, M de Rijke Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 19 | 2021 |
| 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 | 19 | 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 | 16 | 2020 |
| Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness H Oosterhuis Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021 | 14 | 2021 |
| Optimizing ranking models in an online setting H Oosterhuis, M Rijke European Conference on Information Retrieval, 382-396, 2019 | 12 | 2019 |
| Semantic video trailers H Oosterhuis, S Ravi, M Bendersky ICML 2016 Workshop on Multi-View Representation Learning, 2016 | 12 | 2016 |
| 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 | 8 | 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 | 8 | 2020 |
| Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank H Oosterhuis, M de Rijke Proceedings of the Web Conference 2021, 158-170, 2021 | 7 | 2021 |
Maarten de RijkeUniversity of Amsterdam & ICAIVerified email at uva.nl
