Curriculum Vitae
According to my Google Scholar profile my publications have received over 600 citations and my h-index is 15.
H. Oosterhuis. "Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank." ACM Transactions on Information Systems 41.3 (2023): 1-33.
S. Gupta, H. Oosterhuis, and M. de Rijke. "VAE-IPS: A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback." CONSEQUENCES+REVEAL Workshop at RecSys ’22, September 23, 2022, Seattle, USA.
C. Rus, J. Luppes, H. Oosterhuis and G. Schoenmacker. "Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation." RecSys in HR’22: The 2nd Workshop on Recommender Systems for Human Resources, in conjunction with the 16th ACM Conference on Recommender Systems, September 22, 2022, Seattle, USA.
H. Oosterhuis. "Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract)." In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, 2022.
H. Oosterhuis. "Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity." In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022.
J. Huang, H. Oosterhuis, B. Cetinkaya, T. Rood and M. de Rijke. "State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study." In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2022.
H. Oosterhuis. "Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank." In Proceedings of the 2022 ACM SIGIR International Conference on the Theory of Information Retrieval. ACM, 2022.
N. Knyazev and H. Oosterhuis. "The Bandwagon Effect: Not Just Another Bias." In Proceedings of the 2022 ACM SIGIR International Conference on the Theory of Information Retrieval. ACM, 2022.
A. Lucic, H. Oosterhuis, H. Haned and M. de Rijke. "FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles." In AAAI 2022: Thirty-Sixth AAAI Conference on Artificial Intelligence. 2022.
J. Huang, H. Oosterhuis and M. de Rijke. "It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic." In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM ’22). ACM, 2021.
H. Oosterhuis and M. de Rijke. "Unifying Online and Counterfactual Learning to Rank (Extended Abstract)." In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, 2021.
H. Oosterhuis. "Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness." In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2021.
H. Oosterhuis and M. de Rijke. "Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank." The World Wide Web Conference. ACM, 2021.
H. Oosterhuis and M. de Rijke. "Unifying Online and Counterfactual Learning to Rank." In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM ’21). ACM, 2021.
Harrie Oosterhuis. "Learning from User Interactions with Rankings: A Unification of the Field." PhD thesis, University of Amsterdam, November 2020.
A. Vardasbi, H. Oosterhuis, M. de Rijke. "When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM, 2020.
J. Huang, H. Oosterhuis, M. de Rijke, and H. van Hoof. "Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems." In Fourteenth ACM Conference on Recommender Systems, pp. 190-199. ACM, 2020.
H. Oosterhuis, and M. de Rijke. "Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking." In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. ACM, 2020.
H. Oosterhuis, and M. de Rijke. "Policy-Aware Unbiased Learning to Rank for Top-k Rankings." In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020.
H. Oosterhuis, R. Jagerman, and M. de Rijke. "Unbiased Learning to Rank: Counterfactual and Online Approaches." In Companion Proceedings of the Web Conference 2020. ACM, 2020.
C. Lucchese, F. M. Nardini, R. K. Pasumarthi, S. Bruch, M. Bendersky, X. Wang, H. Oosterhuis, R. Jagerman, M. de Rijke. "Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning." In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019.
R. Jagerman, H. Oosterhuis and M. de Rijke. "To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions." In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2019.
H. Oosterhuis, M. de Rijke. "Optimizing Ranking Models in an Online Setting." In European Conference on Information Retrieval. Springer, Cham, 2019.
H. Oosterhuis, J. S. Culpepper, M. de Rijke. "The Potential of Learned Index Structures for Index Compression." In Proceedings of the 23rd Australasian Document Computing Symposium. ACM, 2018.
H. Oosterhuis, M. de Rijke. "Differentiable Unbiased Online Learning to Rank." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018.
H. Oosterhuis, M. de Rijke. "Ranking for Relevance and Display Preferences in Complex Presentation Layouts." In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 2018.
Z. Li, A. Grotov, J. Kiseleva, M. de Rijke, H. Oosterhuis. "Optimizing Interactive Systems with Data-Driven Objectives." In arXiv preprint. 2018.
H. Oosterhuis, M. de Rijke. "Sensitive and Scalable Online Evaluation with Theoretical Guarantees." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
H. Oosterhuis, M. de Rijke. "Balancing Speed and Quality in Online Learning to Rank for Information Retrieval." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017.
R. Jagerman, H. Oosterhuis, M. de Rijke. "Query-level Ranker Specialization." In Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers, co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR ’2017). ACM, 2017.
H. Oosterhuis, S. Ravi, M. Bendersky. "Query-level Ranker Specialization." In ICML 2016 Workshop on Multi-View Representation Learning. 2016.
A. Schuth, H. Oosterhuis, S. Whiteson, M. de Rijke. "Multileave Gradient Descent for Fast Online Learning to Rank." In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 2016.
H. Oosterhuis, M. de Rijke. "Probabilistic Multileave Gradient Descent." In European Conference on Information Retrieval. Springer, Cham, 2016.
A. Schuth, R. Bruintjes, F. Büttner, J. van Doorn, C. Groenland, H. Oosterhuis, C. Tran, B. Veeling, J. van der Velde, R. Wechsler, D. Woudenberg, M. de Rijke. "Probabilistic Multileave for Online Retrieval Evaluation." In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015.
Invited Seminar Talk at Search Engines Amsterdam (SEA), University of Amsterdam (Online Event), Amsterdam, The Netherlands (Online Event)
Workshop Course at SIKS Course: Advances in Information Retrieval, Netherlands Research School for Information and Knowledge Systems, Berg en Dal, The Netherlands
Invited Seminar Talk at University of Glasgow, Information Retrieval Seminar, Glasgow, United Kingdom (Online Event)
Invited Talk at Twitter Research, London, United Kingdom (Online Event)
Invited Seminar Talk at CIIR talk series, UMass Amherst, United States (Online Event)
Conference Proceedings Talk at the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), Online Event
Invited Talk at Google Mountain View, Mountain View, California, United States (Online Event)
Invited Seminar Talk at the Florence Nightingale Colloquium at the Leiden University, Leiden, The Netherlands (Online Event)
Conference Proceedings Talk at the 14th ACM International Conference on Web Search and Data Mining (WSDM ’21), Online Event
Guest Lecture at Radboud University, Nijmegen, The Netherlands
Invited Workshop Talk at the 19th Dutch-Belgian Information Retrieval Workshop (DIR ’20), Online Event
Conference Proceedings Talk at the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR ’20), Online Event
Conference Proceedings Talk at the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20), Online Event
Invited Talk at Farfetch, Porto, Portugal (Online Event)
Invited Talk at Microsoft, London, United Kingdom (Online Event)
Invited Talk at Radboud University, Nijmegen, The Netherlands
Guest Lecture at Radboud University, Nijmegen, The Netherlands
Invited Workshop Talk at the 18th Dutch-Belgian Information Retrieval Workshop (DIR ’19), Amsterdam, The Netherlands
Invited Talk at the PyData Meetup, Amsterdam, The Netherlands
Conference Tutorial at the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19), Paris, France
Conference Proceedings Talk at European Conference on Information Retrieval (ECIR ’19), Cologne, Germany
Invited Talk at University of Glasgow, Glasgow, United Kingdom
Invited Talk at the Search Engines Amsterdam Meetup, Amsterdam, the Netherlands
Invited Talk at SEEK, Melbourne, Australia
Summer School Course at the Russian Summer School in Information Retrieval 2018 (RuSSIR ’18), Kazan, Russia