Harrie Oosterhuis
Titel
Zitiert von
Zitiert von
Jahr
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
532016
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
242019
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
242015
Differentiable unbiased online learning to rank
H Oosterhuis, M de Rijke
Proceedings of the 27th ACM International Conference on Information and …, 2018
232018
Probabilistic multileave gradient descent
H Oosterhuis, A Schuth, M de Rijke
European Conference on Information Retrieval, 661-668, 2016
212016
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
172018
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
162017
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
142017
The Potential of Learned Index Structures for Index Compression
H Oosterhuis, JS Culpepper, M de Rijke
Australasian Document Computing Symposium (ADCS) 23, 2018
122018
Semantic video trailers
H Oosterhuis, S Ravi, M Bendersky
ICML 2016 Workshop on Multi-View Representation Learning, 2016
92016
Optimizing Ranking Models in an Online Setting
H Oosterhuis, M de Rijke
European Conference on Information Retrieval, 382-396, 2019
82019
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles
A Lucic, H Oosterhuis, H Haned, M de Rijke
arXiv preprint arXiv:1911.12199, 2019
62019
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
H Oosterhuis, M de Rijke
arXiv preprint arXiv:2005.09035, 2020
52020
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
42020
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
32019
Query-level Ranker Specialization
R Jagerman, H Oosterhuis, M de Rijke
1st International Workshop on LEARning Next gEneration Rankers (LEARNER), 2017
32017
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
22020
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
22020
Unifying Online and Counterfactual Learning to Rank
H Oosterhuis, M de Rijke
arXiv preprint arXiv:2012.04426, 2020
12020
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
12020
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