B. Á. Pires and C. Szepesvári. Multiclass classification calibration functions. arXiv preprint arXiv:1609.06385, 2016.
B. A. Pires, C. Szepesvari, and M. Ghavamzadeh. Cost-sensitive multiclass classification risk bounds. In International Conference on Machine Learning, pages 1391-1399, 2013.
H. G. Ramaswamy and S. Agarwal. Classification calibration dimension for general multiclass losses. In Advances in Neural Information Processing Systems, 2012.
H. G. Ramaswamy and S. Agarwal. Convex calibration dimension for multiclass loss matrices. Journal of Machine Learning Research, 17(1):397-441, 2016.
H. G. Ramaswamy, S. Agarwal, and A. Tewari. Convex calibrated surrogates for low-rank loss matrices with applications to subset ranking losses. In Advances in Neural Information Processing Systems, 2013.
H. G. Ramaswamy, A. Tewari, and S. Agarwal. Consistent algorithms for multiclass classification with a reject option. arXiv preprint arXiv:1505.04137, 2015.
P. Ravikumar, A. Tewari, and E. Yang. On NDCG consistency of listwise ranking methods. In International Conference on Artificial Intelligence and Statistics, pages 618-626, 2011.
I. Steinwart. How to compare different loss functions and their risks. Constructive Approximation, 26(2):225-287, 2007.
A. Tewari and P. L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8(36):1007-1025, 2007.
A. Thilagar, R. Frongillo, J. J. Finocchiaro, and E. Goodwill. Consistent polyhedral surrogates for top-k classification and variants. In International Conference on Machine Learning, pages 21329-21359, 2022.
K. Uematsu and Y. Lee. On theoretically optimal ranking functions in bipartite ranking. Journal of the American Statistical Association, 112(519):1311-1322, 2017.
P. F. Verhulst. Notice sur la loi que la population suit dans son accroissement. Correspondance mathématique et physique, 10:113-121, 1838.
P. F. Verhulst. Recherches mathématiques sur la loi d'accroissement de la population. Nouveaux Mémoires de l'Académie Royale des Sciences et Belles-Lettres de Bruxelles, 18:1-42, 1845.
Y. Wang and C. Scott. Weston-Watkins hinge loss and ordered partitions. In Advances in neural information processing systems, pages 19873-19883, 2020.
Y. Wang and C. D. Scott. On classification-calibration of gamma-phi losses. arXiv preprint arXiv:2302.07321, 2023.
J. Weston and C. Watkins. Multi-class support vector machines. Technical report, Citeseer, 1998.
R. C. Williamson, E. Vernet, and M. D. Reid. Composite multiclass losses. Journal of Machine Learning Research, 17:1-52, 2016.
M. Zhang and S. Agarwal. Bayes consistency vs. H-consistency: The interplay between surrogate loss functions and the scoring function class. In Advances in Neural Information Processing Systems, pages 16927-16936, 2020.
M. Zhang, H. G. Ramaswamy, and S. Agarwal. Convex calibrated surrogates for the multi-label f-measure. In International Conference on Machine Learning, pages 11246-11255, 2020.
T. Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization. The Annals of Statistics, 32(1):56-85, 2004a.
T. Zhang. Statistical analysis of some multi-category large margin classification methods. Journal of Machine Learning Research, 5(Oct):1225-1251, 2004b.