Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision

By David Buffoni, Clément Calauzènes, Patrick Gallinari, Nicolas Usunier. 2011

38 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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Citations in the corpus

Quantifying Learning Guarantees for Convex but Inconsistent Surrogates (2018, methodology): “Consistency of convex surrogates has been the central question of many studies for such problems as binary classification [2, 24, 19], multi-class classification [23, 21, 1, 17], ranking [11, 4, 5, 18, 15] and, more recently, structured prediction [7, 14].”
On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017, background): “The task of ranking has attracted a lot of attention and [18, 8, 9, 40] analyze different families of surrogate and task losses proving their (in-)consistency.”
Surrogate regret bounds for bipartite ranking via strongly proper losses (2012, background): “Recently, there has been much interest in understanding statistical consistency and regret behavior of algorithms for a variety of ranking problems, including various forms of label/subset ranking as well as instance ranking problems [10, 8, 12, 3, 2, 30, 13, 21, 5, 9, 19, 29].”
"On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking" (2012, background): “Signiﬁcant improvements in performances may then be obtained by switching to other approaches than the optimization of a convex risk.”
A Theoretical Analysis of NDCG Type Ranking Measures (2013, result): “istency for ranking was initiated in [14, 17]. In fact, [17] showed that no convex surrogate loss can be consistent with the Pairwise Disagreement (PD) measure. This result was further generalized in [7, 9], where non-existence of convex surrogate loss with Average Precision and Expected Reciprocal ”
A cross-benchmark comparison of 87 learning to rank methods (2015, background): “The study did not report any baseline performance that allowed us to check validity of the results (Chakrabarti et al., 2008; Wang et al., 2012b; Buffoni et al., 2011).”
Consistent Multiclass Algorithms for Complex Performance Measures (2015, background): “, 2004; Tewari & Bartlett, 2007), losses for specific problems such as multilabel classification (Gao & Zhou, 2011) and ranking (Cossock & Zhang, 2008; Xia et al., 2008; Duchi et al., 2010; Ravikumar et al., 2011; Buffoni et al., 2011; Calauzènes et al., 2012), and some work on general multiclass lo”
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses (2013, methodology): “We have given an explicit method for constructing calibrated surrogates for any learning problem with a low-rank loss structure, and have used this to obtain several new results for subset ranking, including new calibrated surrogates for the Precision@q, ERU, MAP and PD losses.”