| 2026 | ECIR | Sample-Free Almost-Exact Estimation of Plackett-Luce Propensities for Off-Policy Ranking. | Norman Knyazev, Harrie Oosterhuis |
| 2026 | ICTIR | Exposure-Based Reinforcement Learning to Rank. | Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang |
| 2026 | ICTIR | Prior-Data Fitted Networks as Tabular Foundation Models for Ranking in Low-Data Settings. | David Vos, Samarth Bhargav, Maarten de Rijke, Harrie Oosterhuis |
| 2026 | SIGIR | Following the Eye-Tracking Evidence: Established Web-Search Assumptions Fail in Carousel Interfaces. | Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis |
| 2026 | SIGIR | An Epistemic Position-Based Click Model: From Interactions to Epistemic Distributions of Relevance and Bias. | Oscar Rolando Ramirez Milian, Harrie Oosterhuis |
| 2025 | ICTIR | Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models. | Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez, Harrie Oosterhuis |
| 2025 | RecSys | A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options. | Thorsten Krause, Harrie Oosterhuis |
| 2025 | RecSys | CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Harrie Oosterhuis, Olivier Jeunen, Yuta Saito, Yixin Wang, Flavian Vasile, Thorsten Joachims |
| 2025 | SIGIR | Adaptive Orchestration of Modular Generative Information Access Systems. | Mohanna Hoveyda, Harrie Oosterhuis, Arjen P. de Vries, Maarten de Rijke, Faegheh Hasibi |
| 2025 | SIGIR | Learning to Rank with Variable Result Presentation Lengths. | Norman Knyazev, Harrie Oosterhuis |
| 2025 | SIGIR | RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces. | Santiago de Leon-Martinez, Jingwei Kang, Rbert Mro, Maarten de Rijke, Branislav Kveton, Harrie Oosterhuis, Mria Bielikov |
| 2025 | SIGIR | Optimizing Compound Retrieval Systems. | Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang |
| 2024 | CIKM | Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank. | Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke |
| 2024 | ECIR | Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? | Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand |
| 2024 | EMNLP | Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. | Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis |
| 2024 | ICML | Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions. | Harrie Oosterhuis, Lijun Lyu, Avishek Anand |
| 2024 | KDD | Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. | Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky |
| 2024 | RecSys | Optimal Baseline Corrections for Off-Policy Contextual Bandits. | Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke |
| 2024 | RecSys | CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Olivier Jeunen, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang |
| 2024 | SIGIR | Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. | Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke |
| 2024 | SIGIR | Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees. | Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis |
| 2024 | WSDM | Unbiased Learning to Rank: On Recent Advances and Practical Applications. | Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis |
| 2023 | ICTIR | A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback. | Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke |
| 2023 | RecSys | CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang |
| 2023 | RecSys | A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. | Norman Knyazev, Harrie Oosterhuis |
| 2023 | SIGIR | Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. | Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis |
| 2023 | SIGIR | Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. | Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke |
| 2022 | AAAI | FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. | Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke |
| 2022 | IJCAI | Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract). | Harrie Oosterhuis |
| 2022 | ICTIR | The Bandwagon Effect: Not Just Another Bias. | Norman Knyazev, Harrie Oosterhuis |
| 2022 | ICTIR | Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank. | Harrie Oosterhuis |
| 2022 | RecSys | CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. | Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile |
| 2022 | SIGIR | State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. | Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke |
| 2022 | SIGIR | Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity. | Harrie Oosterhuis |
| 2022 | WSDM | It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. | Jin Huang, Harrie Oosterhuis, Maarten de Rijke |
| 2021 | IJCAI | Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract). | Harrie Oosterhuis, Maarten de Rijke |
| 2021 | ICTIR | Session details: Session 4B - Semantic Retrieval. | Harrie Oosterhuis |
| 2021 | WWW | Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank. | Harrie Oosterhuis, Maarten de Rijke |
| 2021 | SIGIR | Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. | Harrie Oosterhuis |
| 2021 | WSDM | Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. | Harrie Oosterhuis, Maarten de Rijke |
| 2020 | CIKM | When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. | Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke |
| 2020 | ICTIR | Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. | Harrie Oosterhuis, Maarten de Rijke |
| 2020 | RecSys | Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. | Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof |
| 2020 | WWW | Unbiased Learning to Rank: Counterfactual and Online Approaches. | Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke |
| 2020 | SIGIR | Policy-Aware Unbiased Learning to Rank for Top-k Rankings. | Harrie Oosterhuis, Maarten de Rijke |
| 2019 | ECIR | Optimizing Ranking Models in an Online Setting. | Harrie Oosterhuis, Maarten de Rijke |
| 2019 | SIGIR | To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. | Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke |
| 2019 | SIGIR | Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. | Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke |
| 2018 | ADCS | The Potential of Learned Index Structures for Index Compression. | Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke |
| 2018 | CIKM | Differentiable Unbiased Online Learning to Rank. | Harrie Oosterhuis, Maarten de Rijke |
| 2018 | SIGIR | Ranking for Relevance and Display Preferences in Complex Presentation Layouts. | Harrie Oosterhuis, Maarten de Rijke |
| 2017 | CIKM | Sensitive and Scalable Online Evaluation with Theoretical Guarantees. | Harrie Oosterhuis, Maarten de Rijke |
| 2017 | CIKM | Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. | Harrie Oosterhuis, Maarten de Rijke |
| 2017 | ICTIR | Query-Level Ranker Specialization. | Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke |
| 2016 | ECIR | Probabilistic Multileave Gradient Descent. | Harrie Oosterhuis, Anne Schuth, Maarten de Rijke |
| 2016 | WSDM | Multileave Gradient Descent for Fast Online Learning to Rank. | Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, Maarten de Rijke |
| 2015 | SIGIR | Probabilistic Multileave for Online Retrieval Evaluation. | Anne Schuth, Robert-Jan Bruintjes, Fritjof Buttner, Joost van Doorn, Carla Groenland, Harrie Oosterhuis, Cong-Nguyen Tran, Bas Veeling, Jos van der Velde, Roger Wechsler, David Woudenberg, Maarten de Rijke |