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ORCIDRolf Jagerman
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2020 – today
- 2024
[c19]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- [c18]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 - [c17]Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky:
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. NAACL-HLT (Findings) 2024: 1504-1518 - [c16]Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky:
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers? SIGIR 2024: 2321-2326 - [i17]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) - [i16]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) - [i15]Zhenrui Yue, Honglei Zhuang, Aijun Bai, Kai Hui, Rolf Jagerman, Hansi Zeng, Zhen Qin, Dong Wang, Xuanhui Wang, Michael Bendersky:
Inference Scaling for Long-Context Retrieval Augmented Generation. CoRR abs/2410.04343 (2024) - 2023
- [c15]Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork:
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance. CIKM 2023: 4502-4508 - [c14]Zhen Qin, Rolf Jagerman, Rama Kumar Pasumarthi, Honglei Zhuang, He Zhang, Aijun Bai, Kai Hui, Le Yan, Xuanhui Wang:
RD-Suite: A Benchmark for Ranking Distillation. NeurIPS 2023 - [c13]Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky:
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. SIGIR 2023: 2308-2313 - [i14]Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, Michael Bendersky:
Query Expansion by Prompting Large Language Models. CoRR abs/2305.03653 (2023) - [i13]Zhen Qin, Rolf Jagerman, Rama Kumar Pasumarthi, Honglei Zhuang, He Zhang, Aijun Bai, Kai Hui, Le Yan, Xuanhui Wang:
RD-Suite: A Benchmark for Ranking Distillation. CoRR abs/2306.04455 (2023) - [i12]Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky:
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. CoRR abs/2306.17563 (2023) - [i11]Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, Michael Bendersky:
Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword Generation for Zero-Shot Neural Rankers. CoRR abs/2311.09175 (2023) - 2022
- [c12]Rolf Jagerman, Xuanhui Wang, Honglei Zhuang, Zhen Qin, Michael Bendersky, Marc Najork:
Rax: Composable Learning-to-Rank Using JAX. KDD 2022: 3051-3060 - [c11]Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky, Marc Najork:
On Optimizing Top-K Metrics for Neural Ranking Models. SIGIR 2022: 2303-2307 - [i10]Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, Michael Bendersky:
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. CoRR abs/2210.10634 (2022) - [i9]Aijun Bai, Rolf Jagerman, Zhen Qin, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork:
Regression Compatible Listwise Objectives for Calibrated Ranking. CoRR abs/2211.01494 (2022) - 2021
- [c10]Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xinyu Qian, Po Hu, Dan Chary Chen, Xuanhui Wang, Michael Bendersky, Marc Najork:
Bootstrapping Recommendations at Chrome Web Store. KDD 2021: 3483-3491 - [c9]Rolf Jagerman, Weize Kong, Rama Kumar Pasumarthi, Zhen Qin, Michael Bendersky, Marc Najork:
Improving Cloud Storage Search with User Activity. WSDM 2021: 508-516 - 2020
- [j2]Rolf Jagerman, Ilya Markov, Maarten de Rijke:
Safe Exploration for Optimizing Contextual Bandits. ACM Trans. Inf. Syst. 38(3): 24:1-24:23 (2020) - [c8]Rolf Jagerman, Maarten de Rijke:
Accelerated Convergence for Counterfactual Learning to Rank. SIGIR 2020: 469-478 - [c7]Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Unbiased Learning to Rank: Counterfactual and Online Approaches. WWW (Companion Volume) 2020: 299-300 - [i8]Rolf Jagerman, Ilya Markov, Maarten de Rijke:
Safe Exploration for Optimizing Contextual Bandits. CoRR abs/2002.00467 (2020) - [i7]Rolf Jagerman, Maarten de Rijke:
Accelerated Convergence for Counterfactual Learning to Rank. CoRR abs/2005.10615 (2020)
2010 – 2019
- 2019
- [c6]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 - [c5]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 - [c4]Rolf Jagerman, Ilya Markov, Maarten de Rijke:
When People Change their Mind: Off-Policy Evaluation in Non-stationary Recommendation Environments. WSDM 2019: 447-455 - [i6]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) - [i5]Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke:
Unbiased Learning to Rank: Counterfactual and Online Approaches. CoRR abs/1907.07260 (2019) - 2018
- [j1]Rolf Jagerman, Krisztian Balog, Maarten de Rijke:
OpenSearch: Lessons Learned from an Online Evaluation Campaign. ACM J. Data Inf. Qual. 10(3): 13:1-13:15 (2018) - 2017
- [c3]Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke:
Query-Level Ranker Specialization. LEARNER@ICTIR 2017 - [c2]Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke:
Computing Web-scale Topic Models using an Asynchronous Parameter Server. SIGIR 2017: 1337-1340 - [c1]Rolf Jagerman, Krisztian Balog, Philipp Schaer, Johann Schaible, Narges Tavakolpoursaleh, Maarten de Rijke:
Overview of TREC OpenSearch 2017. TREC 2017 - [i4]Rolf Jagerman, Julia Kiseleva, Maarten de Rijke:
Modeling Label Ambiguity for Neural List-Wise Learning to Rank. CoRR abs/1707.07493 (2017) - 2016
- [i3]Rolf Jagerman, Carsten Eickhoff:
Web-scale Topic Models in Spark: An Asynchronous Parameter Server. CoRR abs/1605.07422 (2016) - 2015
- [i2]Jan Deriu, Rolf Jagerman, Kai-En Tsay:
A Directional Diffusion Algorithm for Inpainting. CoRR abs/1511.03464 (2015) - 2014
- [i1]Rolf Jagerman, Wendo Sabée, Laurens Versluis, Martijn de Vos, Johan A. Pouwelse:
The fifteen year struggle of decentralizing privacy-enhancing technology. CoRR abs/1404.4818 (2014)
Coauthor Index
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