The fifteen year struggle of decentralizing privacy-enhancing technology. - Rolf Jagerman; Wendo Sabée; Laurens Versluis; Martijn de Vos; Johan A. Pouwelse (CoRR, 2014)

A Directional Diffusion Algorithm for Inpainting. - Jan Deriu; Rolf Jagerman; Kai-En Tsay (CoRR, 2015)

Web-scale Topic Models in Spark: An Asynchronous Parameter Server. - Rolf Jagerman; Carsten Eickhoff (CoRR, 2016)

Query-Level Ranker Specialization. - Rolf Jagerman; Harrie Oosterhuis; Maarten de Rijke (LEARNER@ICTIR, 2017)

Computing Web-scale Topic Models using an Asynchronous Parameter Server. - Rolf Jagerman; Carsten Eickhoff; Maarten de Rijke (SIGIR, 2017)

Overview of TREC OpenSearch 2017. - Rolf Jagerman; Krisztian Balog; Philipp Schaer; Johann Schaible; Narges Tavakolpoursaleh; Maarten de Rijke (TREC, 2017)

Modeling Label Ambiguity for Neural List-Wise Learning to Rank. - Rolf Jagerman; Julia Kiseleva; Maarten de Rijke (CoRR, 2017)

OpenSearch: Lessons Learned from an Online Evaluation Campaign. - Rolf Jagerman; Krisztian Balog; Maarten de Rijke (ACM J. Data Inf. Qual., 2018)

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. - Rolf Jagerman; Harrie Oosterhuis; Maarten de Rijke (SIGIR, 2019)

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 001; Michael Bendersky; Xuanhui Wang; Harrie Oosterhuis; Rolf Jagerman; Maarten de Rijke (SIGIR, 2019)

When People Change their Mind: Off-Policy Evaluation in Non-stationary Recommendation Environments. - Rolf Jagerman; Ilya Markov; Maarten de Rijke (WSDM, 2019)

Accelerated Convergence for Counterfactual Learning to Rank. - Rolf Jagerman; Maarten de Rijke (SIGIR, 2020)

Unbiased Learning to Rank: Counterfactual and Online Approaches. - Harrie Oosterhuis; Rolf Jagerman; Maarten de Rijke (WWW (Companion Volume), 2020)

Safe Exploration for Optimizing Contextual Bandits. - Rolf Jagerman; Ilya Markov; Maarten de Rijke (ACM Trans. Inf. Syst., 2020)

Bootstrapping Recommendations at Chrome Web Store. - Zhen Qin 001; Honglei Zhuang; Rolf Jagerman; Xinyu Qian; Po Hu; Dan Chary Chen; Xuanhui Wang; Michael Bendersky; Marc Najork (KDD, 2021)

Improving Cloud Storage Search with User Activity. - Rolf Jagerman; Weize Kong; Rama Kumar Pasumarthi; Zhen Qin 001; Michael Bendersky; Marc Najork (WSDM, 2021)

Rax: Composable Learning-to-Rank Using JAX. - Rolf Jagerman; Xuanhui Wang; Honglei Zhuang; Zhen Qin 001; Michael Bendersky; Marc Najork (KDD, 2022)

On Optimizing Top-K Metrics for Neural Ranking Models. - Rolf Jagerman; Zhen Qin 001; Xuanhui Wang; Michael Bendersky; Marc Najork (SIGIR, 2022)

Regression Compatible Listwise Objectives for Calibrated Ranking. - Aijun Bai; Rolf Jagerman; Zhen Qin 001; Pratyush Kar; Bing-Rong Lin; Xuanhui Wang; Michael Bendersky; Marc Najork (CoRR, 2022)

Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance. - Aijun Bai; Rolf Jagerman; Zhen Qin 001; Le Yan; Pratyush Kar; Bing-Rong Lin; Xuanhui Wang; Michael Bendersky; Marc Najork (CIKM, 2023)

RD-Suite: A Benchmark for Ranking Distillation. - Zhen Qin 001; Rolf Jagerman; Rama Kumar Pasumarthi; Honglei Zhuang; He Zhang; Aijun Bai; Kai Hui 001; Le Yan; Xuanhui Wang (NeurIPS, 2023)

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. - Honglei Zhuang; Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Ji Ma 004; Jing Lu; Jianmo Ni; Xuanhui Wang; Michael Bendersky (SIGIR, 2023)

Query Expansion by Prompting Large Language Models. - Rolf Jagerman; Honglei Zhuang; Zhen Qin 001; Xuanhui Wang; Michael Bendersky (CoRR, 2023)

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. - Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Honglei Zhuang; Junru Wu; Jiaming Shen; Tianqi Liu 002; Jialu Liu; Donald Metzler; Xuanhui Wang; Michael Bendersky (CoRR, 2023)

Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers. - Minghan Li 002; Honglei Zhuang; Kai Hui 001; Zhen Qin 001; Jimmy Lin; Rolf Jagerman; Xuanhui Wang; Michael Bendersky (CoRR, 2023)

Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. - Le Yan; Zhen Qin 001; Honglei Zhuang; Rolf Jagerman; Xuanhui Wang; Michael Bendersky; Harrie Oosterhuis (EMNLP, 2024)

Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. - Harrie Oosterhuis; Rolf Jagerman; Zhen Qin 001; Xuanhui Wang; Michael Bendersky (KDD, 2024)

Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. - Zhen Qin 001; Rolf Jagerman; Kai Hui 001; Honglei Zhuang; Junru Wu; Le Yan; Jiaming Shen; Tianqi Liu 002; Jialu Liu; Donald Metzler; Xuanhui Wang; Michael Bendersky (NAACL-HLT (Findings), 2024)

Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers? - Minghan Li 002; Honglei Zhuang; Kai Hui 001; Zhen Qin 001; Jimmy Lin; Rolf Jagerman; Xuanhui Wang; Michael Bendersky (SIGIR, 2024)

Inference Scaling for Long-Context Retrieval Augmented Generation. - Zhenrui Yue; Honglei Zhuang; Aijun Bai; Kai Hui 001; Rolf Jagerman; Hansi Zeng; Zhen Qin 001; Dong Wang 002; Xuanhui Wang; Michael Bendersky (ICLR, 2025)

Optimizing Compound Retrieval Systems. - Harrie Oosterhuis; Rolf Jagerman; Zhen Qin 001; Xuanhui Wang (SIGIR, 2025)