default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDHarrie Oosterhuis
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c47]Shashank Gupta
, Harrie Oosterhuis, Maarten de Rijke:
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank. CIKM 2024: 737-747- [c46]Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand:
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? ECIR (4) 2024: 384-402 - [c45]Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis:
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. EMNLP 2024: 410-423 - [c44]Harrie Oosterhuis, Lijun Lyu, Avishek Anand:
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions. ICML 2024 - [c43]Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky:
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. KDD 2024: 2307-2317 - [c42]Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke:
Optimal Baseline Corrections for Off-Policy Contextual Bandits. RecSys 2024: 722-732 - [c41]Olivier Jeunen, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang:
CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys 2024: 1206-1209 - [c40]Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke:
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. SIGIR 2024: 416-426 - [c39]Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis:
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees. SIGIR 2024: 2390-2394 - [c38]Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis:
Unbiased Learning to Rank: On Recent Advances and Practical Applications. WSDM 2024: 1118-1121 - [e1]Harrie Oosterhuis, Hannah Bast, Chenyan Xiong:
Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2024, Washington, DC, USA, 13 July 2024. ACM 2024, ISBN 979-8-4007-0681-3 [contents] - [i40]Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis:
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. CoRR abs/2404.11791 (2024) - [i39]Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis:
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees. CoRR abs/2404.12190 (2024) - [i38]Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke:
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. CoRR abs/2404.18640 (2024) - [i37]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
A First Look at Selection Bias in Preference Elicitation for Recommendation. CoRR abs/2405.00554 (2024) - [i36]Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke:
Optimal Baseline Corrections for Off-Policy Contextual Bandits. CoRR abs/2405.05736 (2024) - [i35]Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand:
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? CoRR abs/2405.07782 (2024) - [i34]Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky:
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. CoRR abs/2407.02464 (2024) - [i33]Harrie Oosterhuis, Lijun Lyu, Avishek Anand:
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions. CoRR abs/2407.11778 (2024) - [i32]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank. CoRR abs/2407.19943 (2024) - [i31]Hua Chang Bakker, Shashank Gupta, Harrie Oosterhuis:
A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization. CoRR abs/2409.09819 (2024) - [i30]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank. CoRR abs/2409.09881 (2024) - [i29]Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke, Harrie Oosterhuis, Faegheh Hasibi:
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit. CoRR abs/2409.13447 (2024) - 2023
- [j2]Harrie Oosterhuis:
Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank. ACM Trans. Inf. Syst. 41(3): 61:1-61:33 (2023) - [c37]Shashank Gupta, Philipp K. Hager, Harrie Oosterhuis:
Recent Advancements in Unbiased Learning to Rank. FIRE 2023: 145-148 - [c36]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback. ICTIR 2023: 87-93 - [c35]Norman Knyazev, Harrie Oosterhuis:
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. RecSys 2023: 306-317 - [c34]Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang:
CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys 2023: 1223-1226 - [c33]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. SIGIR 2023: 249-258 - [c32]Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis:
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. SIGIR 2023: 3440-3443 - [i28]Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke:
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. CoRR abs/2305.01522 (2023) - [i27]Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis:
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. CoRR abs/2305.02914 (2023) - [i26]Norman Knyazev, Harrie Oosterhuis:
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. CoRR abs/2308.03186 (2023) - 2022
- [c31]Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke:
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. AAAI 2022: 5313-5322 - [c30]Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker:
Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. HR@RecSys 2022 - [c29]Norman Knyazev, Harrie Oosterhuis:
The Bandwagon Effect: Not Just Another Bias. ICTIR 2022: 243-253 - [c28]Harrie Oosterhuis:
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank. ICTIR 2022: 264-274 - [c27]Harrie Oosterhuis:
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract). IJCAI 2022: 5319-5323 - [c26]Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile:
CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys 2022: 654-657 - [c25]Harrie Oosterhuis:
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity. SIGIR 2022: 2266-2271 - [c24]Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke:
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. SIGIR 2022: 2738-2748 - [c23]Jin Huang, Harrie Oosterhuis, Maarten de Rijke:
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. WSDM 2022: 381-389 - [i25]Harrie Oosterhuis:
Doubly-Robust Estimation for Unbiased Learning-to-Rank from Position-Biased Click Feedback. CoRR abs/2203.17118 (2022) - [i24]Harrie Oosterhuis:
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity. CoRR abs/2204.10872 (2022) - [i23]Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke:
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. CoRR abs/2205.04797 (2022) - [i22]Harrie Oosterhuis:
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank. CoRR abs/2206.12204 (2022) - [i21]Norman Knyazev, Harrie Oosterhuis:
The Bandwagon Effect: Not Just Another Bias. CoRR abs/2206.12701 (2022) - [i20]Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker:
Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. CoRR abs/2209.09592 (2022) - 2021
- [c22]Harrie Oosterhuis:
Session details: Session 4B - Semantic Retrieval. ICTIR 2021 - [c21]Harrie Oosterhuis, Maarten de Rijke:
Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract). IJCAI 2021: 4809-4813 - [c20]Harrie Oosterhuis:
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. SIGIR 2021: 1023-1032 - [c19]Harrie Oosterhuis, Maarten de Rijke:
Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. WSDM 2021: 463-471 - [c18]Harrie Oosterhuis, Maarten de Rijke:
Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank. WWW 2021: 158-170 - [i19]Harrie Oosterhuis, Maarten de Rijke:
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank. CoRR abs/2102.05990 (2021) - [i18]Harrie Oosterhuis:
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. CoRR abs/2105.00855 (2021) - [i17]Jin Huang, Harrie Oosterhuis, Maarten de Rijke:
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. CoRR abs/2111.12481 (2021) - 2020
- [j1]Harrie Oosterhuis:
Learning from user interactions with rankings: a unification of the field. SIGIR Forum 54(2): 16:1-16:2 (2020) - [c17]Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke:
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. CIKM 2020: 1475-1484 - [c16]Harrie Oosterhuis, Maarten de Rijke:
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. ICTIR 2020: 137-144 - [c15]Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof:
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. RecSys 2020: 190-199 - [c14]Harrie Oosterhuis, Maarten de Rijke:
Policy-Aware Unbiased Learning to Rank for Top-k Rankings. SIGIR 2020: 489-498 - [c13]Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Unbiased Learning to Rank: Counterfactual and Online Approaches. WWW (Companion Volume) 2020: 299-300 - [i16]Harrie Oosterhuis, Maarten de Rijke:
Policy-Aware Unbiased Learning to Rank for Top-k Rankings. CoRR abs/2005.09035 (2020) - [i15]Harrie Oosterhuis, Maarten de Rijke:
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. CoRR abs/2007.12719 (2020) - [i14]Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke:
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. CoRR abs/2008.10242 (2020) - [i13]Harrie Oosterhuis, Maarten de Rijke:
Unifying Online and Counterfactual Learning to Rank. CoRR abs/2012.04426 (2020) - [i12]Harrie Oosterhuis:
Learning from User Interactions with Rankings: A Unification of the Field. CoRR abs/2012.06576 (2020)
2010 – 2019
- 2019
- [c12]Harrie Oosterhuis, Maarten de Rijke:
Optimizing Ranking Models in an Online Setting. ECIR (1) 2019: 382-396 - [c11]Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke:
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. SIGIR 2019: 15-24 - [c10]Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. SIGIR 2019: 1419-1420 - [i11]Harrie Oosterhuis, Maarten de Rijke:
Optimizing Ranking Models in an Online Setting. CoRR abs/1901.10262 (2019) - [i10]Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke:
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. CoRR abs/1907.06412 (2019) - [i9]Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Unbiased Learning to Rank: Counterfactual and Online Approaches. CoRR abs/1907.07260 (2019) - [i8]Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke:
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. CoRR abs/1911.12199 (2019) - 2018
- [c9]Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke:
The Potential of Learned Index Structures for Index Compression. ADCS 2018: 7:1-7:4 - [c8]Harrie Oosterhuis, Maarten de Rijke:
Differentiable Unbiased Online Learning to Rank. CIKM 2018: 1293-1302 - [c7]Harrie Oosterhuis, Maarten de Rijke:
Ranking for Relevance and Display Preferences in Complex Presentation Layouts. SIGIR 2018: 845-854 - [i7]Ziming Li, Artem Grotov, Julia Kiseleva, Maarten de Rijke, Harrie Oosterhuis:
Optimizing Interactive Systems with Data-Driven Objectives. CoRR abs/1802.06306 (2018) - [i6]Harrie Oosterhuis, Maarten de Rijke:
Ranking for Relevance and Display Preferences in Complex Presentation Layouts. CoRR abs/1805.02404 (2018) - [i5]Harrie Oosterhuis, Maarten de Rijke:
Differentiable Unbiased Online Learning to Rank. CoRR abs/1809.08415 (2018) - [i4]Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke:
The Potential of Learned Index Structures for Index Compression. CoRR abs/1811.06678 (2018) - 2017
- [c6]Harrie Oosterhuis, Maarten de Rijke:
Sensitive and Scalable Online Evaluation with Theoretical Guarantees. CIKM 2017: 77-86 - [c5]Harrie Oosterhuis, Maarten de Rijke:
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. CIKM 2017: 277-286 - [c4]Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke:
Query-Level Ranker Specialization. LEARNER@ICTIR 2017 - [i3]Harrie Oosterhuis, Maarten de Rijke:
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. CoRR abs/1711.09446 (2017) - [i2]Harrie Oosterhuis, Maarten de Rijke:
Sensitive and Scalable Online Evaluation with Theoretical Guarantees. CoRR abs/1711.09454 (2017) - 2016
- [c3]Harrie Oosterhuis, Anne Schuth, Maarten de Rijke:
Probabilistic Multileave Gradient Descent. ECIR 2016: 661-668 - [c2]Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, Maarten de Rijke:
Multileave Gradient Descent for Fast Online Learning to Rank. WSDM 2016: 457-466 - [i1]Harrie Oosterhuis, Sujith Ravi, Michael Bendersky:
Semantic Video Trailers. CoRR abs/1609.01819 (2016) - 2015
- [c1]Anne Schuth, Robert-Jan Bruintjes, Fritjof Buüttner, Joost van Doorn, Carla Groenland, Harrie Oosterhuis, Cong-Nguyen Tran, Bas Veeling, Jos van der Velde, Roger Wechsler, David Woudenberg, Maarten de Rijke:
Probabilistic Multileave for Online Retrieval Evaluation. SIGIR 2015: 955-958
Coauthor Index
[c47] [c42] [c40] [c39] [i39] [i38] [i37] [i36] [i32] [i30] [i29] [c36] [c33] [i28] [c31] [c24] [c23] [i23] [c21] [c19] [c18] [i19] [i17] [c17] [c16] [c15] [c14] [c13] [i16] [i15] [i14] [i13] [c12] [c11] [c10] [i11] [i10] [i9] [i8] [c9] [c8] [c7] [i7] [i6] [i5] [i4] [c6] [c5] [c4] [i3] [i2] [c3] [c2] [c1]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-02 22:26 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
