The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

By Cynthia Rudin. 2009

171 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Signal Processing

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Citations in the corpus

Learning to Branch in Mixed Integer Programming (2016, methodology): “Recently, discrete optimization has been successfully leveraged to improve machine learning (ML) methodology (Roth and Yih 2005; Bertsimas, King, and Mazumder 2015).”
A Theoretical Analysis of NDCG Type Ranking Measures (2013, background): “There seem to be many ways to deﬁne ranking measures and several evaluation measures have been proposed [11, 32, 4, 1, 28].”
Direct Learning to Rank and Rerank (2018, methodology): “– a (cid:96) = p for some p > 0 : Similar to the P -Norm Push, which uses (cid:96) p norms to focus on the top of the list, the same way as an (cid:96) p norm focuses on the largest elements of a vector (Rudin, 2009a).”
AUC Maximization in the Era of Big Data and AI: A Survey (2022, methodology): “Rudin [142] proposes the p-norm push method for bipartitie ranking, which is to optimize a measure focusing on the left end of the ROC curve aiming to the make the leftmost portion of ROC curve higher.”
A cross-benchmark comparison of 87 learning to rank methods (2015, methodology): “Among useful applications of machine learning based ranking outside web search are automatic text summarization, machine translation, drug discovery and determining the ideal order of maintenance operations (Rudin, 2009).”
Corporate residence fraud detection (2014, background): “Other improvements toward this end, could be to directly optimize for a different loss function [31].”
Integer optimization methods for machine learning (2012, methodology): “As another point of comparison, we introduce a new method called “lp ReverseEngineering” (lpRE) that generalizes the P -Norm Push algorithm for supervised ranking [Rudin, 2009].”
On the Consistency of Ranking Algorithms (2010, methodology): “Rudin (2009) uses a p-norm to emphasize the highest ranked instances.”
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost (2009, methodology): “, 2005) has been extended to a highly nonsymmetric setting, namely the case where the top part of the list is considered more important (Rudin, 2009).”
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering (2012, methodology): “Rudin [20] and Francois et al. [21] point to the differences in effects of data concentration at different p and possibilities of utilizing them for effectively tackling data analysis problems; the latter paper cites a number of useful mathematical properties of Minkowski metric.”
How to reverse-engineer quality rankings (2012, methodology): “2 The p reverse-engineering algorithm As another point of comparison, we introduce a new method called “ p Reverse-Engineering” ( pRE) that generalizes the P -Norm Push algorithm for supervised ranking, developed by Rudin (2009). This algorithm minimizes an objective with two terms, one that “pushes”
A process for predicting manhole events in Manhattan (2010, methodology): “Section 8 discusses a means of evaluation for the final model, and Section 9 exemplifies conferencing between scientists and domain experts, followed by a discussion and conclusion.”