Fetching the paper…
Reading the bibliography…
We introduce a novel framework of ranking with abstention, where the learner can abstain from making prediction at some limited cost $c$.
The meaning and use of the area under a receiver operating characteristic (roc) curve
J. A. Hanley and B. J. McNeil · 1982
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Yurii E Nesterov · 1983
Earlier work this paper cites.
Learning to order things
William W Cohen, Robert E Schapire, and Yoram Singer · 1997
Earlier work this paper cites.
Optimizing search engines using clickthrough data
Thorsten Joachims · 2002
Earlier work this paper cites.
AUC optimization vs. error rate minimization
Corinna Cortes and Mehryar Mohri · 2003
Earlier work this paper cites.
An efficient boosting algorithm for combining preferences
Yoav Freund, Raj Iyer, Robert E Schapire, and Yoram Singer · 2003
Earlier work this paper cites.
Statistical behavior and consistency of classification methods based on convex risk minimization
Tong Zhang · 2004
Earlier work this paper cites.
Generalization bounds for the area under the ROC curve
Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth, and Michael I Jordan · 2005
Earlier work this paper cites.
Margin-based ranking meets boosting in the middle
Cynthia Rudin, Corinna Cortes, Mehryar Mohri, and Robert E Schapire · 2005
Earlier work this paper cites.
Convexity, classification, and risk bounds
Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe · 2006
Earlier work this paper cites.
How to compare different loss functions and their risks
Ingo Steinwart · 2007
Earlier work this paper cites.
On the consistency of multiclass classification methods
Ambuj Tewari and Peter L. Bartlett · 2007
Earlier work this paper cites.
An efficient reduction of ranking to classification
Nir Ailon and Mehryar Mohri · 2008
Earlier work this paper cites.
Ranking and empirical minimization of U-statistics
Stéphan Clemençon, Gábor Lugosi, and Nicolas Vayatis · 2008
Earlier work this paper cites.
Statistical analysis of bayes optimal subset ranking
David Cossock and Tong Zhang · 2008
Earlier work this paper cites.
Listwise approach to learning to rank: theory and algorithm
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li · 2008
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Cited alongside, same era.
Preference-based learning to rank
Nir Ailon and Mehryar Mohri · 2010
Cited alongside, same era.
On the consistency of ranking algorithms
John C Duchi, Lester W Mackey, and Michael I Jordan · 2010
Cited alongside, same era.
Learning scoring functions with order-preserving losses and standardized supervision
David Buffoni, Clément Calauzenes, Patrick Gallinari, and Nicolas Usunier · 2011
Cited alongside, same era.
On the consistency of multi-label learning
Wei Gao and Zhi-Hua Zhou · 2011
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Later among the works it cites.
Multi-class deep boosting
Vitaly Kuznetsov, Mehryar Mohri, and Umar Syed · 2014
Later among the works it cites.
Bayes-optimal scorers for bipartite ranking
Aditya Krishna Menon and Robert C Williamson · 2014
Later among the works it cites.
On the consistency of output code based learning algorithms for multiclass learning problems
Harish G Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, and Robert C Williamson · 2014
Later among the works it cites.
On the consistency of AUC pairwise optimization
Wei Gao and Zhi-Hua Zhou · 2015
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bipartite ranking through minimization of univariate loss
Wojciech Kotlowski, Krzysztof J Dembczynski, and Eyke Huellermeier · 2011
Cited alongside, same era.
On ndcg consistency of listwise ranking methods
Pradeep Ravikumar, Ambuj Tewari, and Eunho Yang · 2011
Cited alongside, same era.
On the (non-) existence of convex, calibrated surrogate losses for ranking
Clément Calauzenes, Nicolas Usunier, and Patrick Gallinari · 2012
Cited alongside, same era.
Statistical consistency of ranking methods in a rank-differentiable probability space
Yanyan Lan, Jiafeng Guo, Xueqi Cheng, and Tie-Yan Liu · 2012
Cited alongside, same era.
Classification calibration dimension for general multiclass losses
Harish G Ramaswamy and Shivani Agarwal · 2012
Cited alongside, same era.
One-pass auc optimization
Wei Gao, Rong Jin, Shenghuo Zhu, and Zhi-Hua Zhou · 2013
Cited alongside, same era.
Later among the works it cites.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Later among the works it cites.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Later among the works it cites.
On theoretically optimal ranking functions in bipartite ranking
Kazuki Uematsu and Yoonkyung Lee · 2017
Later among the works it cites.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
Later among the works it cites.
Bayes consistency vs. H-consistency: The interplay between surrogate loss functions and the scoring function class
Mingyuan Zhang and Shivani Agarwal · 2020
Later among the works it cites.
Convex calibrated surrogates for the multi-label f-measure
Mingyuan Zhang, Harish Guruprasad Ramaswamy, and Shivani Agarwal · 2020
Later among the works it cites.
Revisiting discriminative vs. generative classifiers: Theory and implications
Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, and Jun Zhu · 2023
Closest in time.