Claudio Lucchese

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Sebastian Bruch

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Publications

2024
[j34]
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Sebastian Bruch, Claudio Lucchese, Maria Maistro, Franco Maria Nardini:
Special Section on Efficiency in Neural Information Retrieval. ACM Trans. Inf. Syst. 42(5): 113:1-113:4 (2024)
[c104]
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  - conf/sigir/VecchiatoLNB24
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Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch:
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. SIGIR 2024: 2261-2265
[i24]
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  - journals/corr/abs-2404-11731
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Thomas Vecchiato, Claudio Lucchese, Franco Maria Nardini, Sebastian Bruch:
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search. CoRR abs/2404.11731 (2024)
2023
[j32]
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  - journals/ftir/BruchLN23
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Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Efficient and Effective Tree-based and Neural Learning to Rank. Found. Trends Inf. Retr. 17(1): 1-123 (2023)
[c96]
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  - conf/webi/0001PBLT23
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Salvatore Orlando, Gabriella Pasi, Sebastian Bruch, Claudio Lucchese, Xiaohui Tao:
Preface WI-IAT 2023. WI/IAT 2023: xviii-xxi
[i21]
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  - journals/corr/abs-2305-08680
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Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Efficient and Effective Tree-based and Neural Learning to Rank. CoRR abs/2305.08680 (2023)
2022
[j29]
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Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
Report on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022. SIGIR Forum 56(2): 12:1-12:14 (2022)
[c89]
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Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini:
ReNeuIR: Reaching Efficiency in Neural Information Retrieval. SIGIR 2022: 3462-3465
2019
[c82]
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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

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