Calibration and regret bounds for order-preserving surrogate losses in learning to rank

DOI10.1007/s10994-013-5382-3zbMath1300.68039OpenAlexW1986908217MaRDI QIDQ2251435

Clément Calauzènes, Patrick Gallinari, Nicolas Usunier

Publication date: 14 July 2014

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-013-5382-3

zbMATH Keywords

calibrationlearning to ranksurrogate regret bounds

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Query-dependent ranking and its asymptotic properties ⋮ Unnamed Item

Uses Software

• SVMstruct

Cites Work

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• The construction of a class of trivariate nonseparable compactly supported orthogonal wavelets
• How to compare different loss functions and their risks
• 10.1162/15324430260185628
• On the Optimality of Conditional Expectation as a Bregman Predictor
• Statistical Analysis of Bayes Optimal Subset Ranking
• 10.1162/1532443041827916
• Learning Theory
• Convexity, Classification, and Risk Bounds

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