Ranking the Best Instances

By Stéphan Clemencon, Nicolas Vayatis. 2006

104 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Evaluation & Benchmarks

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Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, background): “Because the ranking literature often deals with information retrieval applications, many authors define complex ranking criteria that give more weight to the ordering of the best ranking instances (see Burges et al., 2007; Clémençon and Vayatis, 2007).”
Convex Calibration Dimension for Multiclass Loss Matrices (2014, background): “We also point out that in a related but diﬀerent context, consistency of ranking has also been studied in the instance ranking setting [7, 6, 15, 1].”
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].”
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List (2009, background): “There is also a body of work on ROC curves in general, for example, the estimation of confidence bands for ROC curves (Macskassy et al., 2005), and more recent works by Clemençon and Vayatis addressing statistical aspects of ranking problems (e.g., Clemençon and Vayatis, 2007, 2008).”
Learning to rank for information retrieval (2009, unknown): “For example, the consistency for bipartite ranking was studied in [28], which is, however, more like a classification problem and much simpler than real ranking problems.”
Direct Learning to Rank and Rerank (2018, background): “…to approximately minimize the pairwise misranking error, e.g., RankBoost (Fre-und et al, 2003). for predetermined threshold θ : Related to the local AUC or partial AUC, which looks at the area under the leftmost part of the ROC curve only (Clemenc¸on and Vayatis, 2007, 2008; Dodd and Pepe, 2003).”
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost (2009, background): “Several other recent works also consider this type of highly nonsymmetric setting for ranking (Dekel et al., 2004; Cossock and Zhang, 2008; Clemenc¸on and Vayatis, 2007; Shalev-Shwartz and Singer, 2006; Le and Smola, 2007).”
Integer optimization methods for machine learning (2012, unknown): “• a ℓ = ℓ · 1 [ℓ≥t] for some threshold t: local AUC – concentrates at the top of the list [Clemençon and Vayatis, 2007, 2008, Dodd and Pepe, 2003].”