Fetching the paper…
Reading the bibliography…
The problem of maximizing precision at the top of a ranked list, often dubbed Precision@k (prec@k), finds relevance in myriad learning applications such as ranking, multi-label classification, and learning with severe label imbalance.
The perceptron: A probabilistic model for information storage and organization in the brain
Frank Rosenblatt · 1958
Earlier work this paper cites.
On convergence proofs on perceptrons
A.B.J. Novikoff · 1962
Earlier work this paper cites.
Perceptrons: An Introduction to Computational Geometry
Marvin Lee Minsky and Seymour Papert · 1988
Earlier work this paper cites.
Large margin rank boundaries for ordinal regression
R. Herbrich, T. Graepel, and K. Obermayer · 2000
Earlier work this paper cites.
Optimizing search engines using clickthrough data
T. Joachims · 2002
Earlier work this paper cites.
Covering Number Bounds of Certain Regularized Linear Function Classes
Tong Zhang · 2002
Earlier work this paper cites.
An efficient boosting algorithm for combining preferences
Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer · 2003
Earlier work this paper cites.
Concentration inequalities
Stéphane Boucheron, Gábor Lugosi, and Olivier Bousquet · 2004
Earlier work this paper cites.
Learning to rank using gradient descent
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender · 2005
Earlier work this paper cites.
A Support Vector Method for Multivariate Performance Measures
Thorsten Joachims · 2005
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li · 2007
Cited alongside, same era.
Ranking the best instances
Stéphan Clémençon and Nicolas Vayatis · 2007
Cited alongside, same era.
Direct optimization of ranking measures
Quoc V. Le and Alexander J. Smola · 2007
Cited alongside, same era.
Multi-Label Classification: An Overview
Grigorios Tsoumakas and Ioannis Katakis · 2007
Cited alongside, same era.
A support vector method for optimizing average precision
Y. Yue, T. Finley, F. Radlinski, and T. Joachims · 2007
Cited alongside, same era.
Structured Learning for Non-Smooth Ranking Losses
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, and Chiru Bhattacharyya · 2008
Cited alongside, same era.
The Infinite Push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list
S. Agarwal · 2011
Later among the works it cites.
Accuracy at the top
Stephen Boyd, Corinna Cortes, Mehryar Mohri, and Ana Radovanovic · 2012
Later among the works it cites.
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking
Clément Calauzènes, Nicolas Usunier, and Patrick Gallinari · 2012
Later among the works it cites.
Perceptron-like algorithms and generalization bounds for learning to rank
Sougata Chaudhuri and Ambuj Tewari · 2014
Later among the works it cites.
Online and stochastic gradient methods for non-decomposable loss functions
Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain · 2014
Later among the works it cites.
Top rank optimization in linear time
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tighter Bounds for Structured Estimation
Chuong B. Do, Quoc Le, Choon Hui Teo, Olivier Chapelle, and Alex Smola · 2008
Cited alongside, same era.
Hardness of learning halfspaces with noise
Venkatesan Guruswami and Prasad Raghavendra · 2009
Cited alongside, same era.
The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list
C. Rudin · 2009
Cited alongside, same era.
Learning to rank by optimizing NDCG measure
Hamed Valizadegan, Rong Jin, Ruofei Zhang, and Jianchang Mao · 2009
Cited alongside, same era.
A Structural SVM Based Approach for Optimizing Partial AUC
Harikrishna Narasimhan and Shivani Agarwal
Cited in the paper.
SVM pAUC tight \text{SVM}^{\text{tight}}_{\text{pAUC}} : A New Support Vector Method for Optimizing Partial AUC Based on a Tight Convex Upper Bound
Harikrishna Narasimhan and Shivani Agarwal
Cited in the paper.
Nan Li, Rong Jin, and Zhi-Hua Zhou · 2014
Later among the works it cites.
Fastxml: a fast, accurate and stable tree-classifier for extreme multi-label learning
Yashoteja Prabhu and Manik Varma · 2014
Later among the works it cites.
Ranking via robust binary classification
Hyokun Yun, Parameswaran Raman, and S Vishwanathan · 2014
Later among the works it cites.
Online ranking with top-1 feedback
Sougata Chaudhuri and Ambuj Tewari · 2015
Closest in time.
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
Harikrishna Narasimhan, Purushottam Kar, and Prateek Jain · 2015
Closest in time.