Open Access
Volume 28, 2024
Page(s) 227 - 257
Published online 24 May 2024
  1. Y. Freund and R. Schapire, Adaptive Game Playing Using Multiplicative Weights, Vol. 29 (1999) 79–103. [Google Scholar]
  2. S. Dudoit, Y.H. Yang, M.J. Callow and T.P. Speed, Statistical methods for identifying differentially expressed genes in replicated cdna microarray experiments. Statistica Sinica 12 (2002) 111–139. [Google Scholar]
  3. J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kégl, Aggregate features and adaboost for music classification. Mach. Learn. 65 (2006) 473–484. [Google Scholar]
  4. J. Friedman, T. Hastie and R. Tibshirani, Additive logistic regression: a statistical view of boosting. Ann. Statist. 28 (2000) 337–407. [Google Scholar]
  5. J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Statist. (2001) 1189–1232. [Google Scholar]
  6. G. Ridgeway, Generalized boosting models: a guide to the gbm package. (2007). URL [Google Scholar]
  7. T. Chen and C. Guestrin, XGBoost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California, USA, 2016. ACM (2016) 785–794. ISBN 978-1-4503-4232-2. [Google Scholar]
  8. R.E. Schapire and Y. Freund, Boosting: Foundations and Algorithms. Cambridge University Press (2012). [Google Scholar]
  9. G. Biau and B. Cadre, Optimization by gradient boosting (supplementary material), in Advances in Contemporary Statistics and Econometrics: Festschrift in Honor of Christine Thomas-Agnan, edited by A. Daouia and A. Ruiz-Gazen. Springer, Cham (2001) 23–44. [Google Scholar]
  10. L. Breiman, Population theory for boosting ensembles. Ann. Statist. 32 (2004) 1–11. [Google Scholar]
  11. T. Zhang and B. Yu, Boosting with early stopping: convergence and consistency. Ann. Statist. 33 (2005) 1538–1579. [Google Scholar]
  12. P.L. Bartlett and M. Traskin, AdaBoost is consistent. J. Mach. Learn. Res. 8 (2007) 2347–2368. [Google Scholar]
  13. P. Bühlmann and B. Yu, Boosting with the L2 loss: regression and classification. J. Am. Statist. Assoc. 98 (2003) 324–339. [Google Scholar]
  14. S.N. Ethier and T.G. Kurtz, Markov Processes. Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics. John Wiley & Sons, Inc., New York (1986). [Google Scholar]
  15. D.W. Stroock and S.R.S. Varadhan, Multidimensional Diffusion Processes. Classics in Mathematics. Springer-Verlag, Berlin (2006). Reprint of the 1997 edition. [Google Scholar]
  16. A. Dieuleveut, Stochastic approximation in Hilbert spaces. Université Paris sciences et lettres. (2017). English. NNT:2017PSLEE059. tel-01705522v2. [Google Scholar]
  17. H. Maennel, O. Bousquet and S. Gelly, Gradient Descent Quantizes ReLU Network Features. (2018). [Google Scholar]
  18. K. Lyu and J. Li, Gradient descent maximizes the margin of homogeneous neural networks, in International Conference on Learning Representations 2020 (2020). [Google Scholar]
  19. S.L. Smith, B. Dherin, D.G.T. Barrett and S. De, On the origin of implicit regularization in stochastic gradient descent, in International Conference on Learning Representations 2021 (2021). [Google Scholar]
  20. P.-A. Cornillon, N.W. Hengartner and E. Matzner-Løber, Recursive bias estimation for multivariate regression smoothers. ESAIM: PS 18 (2014) 483–502. [Google Scholar]
  21. E. A. Nadaraya, On estimating regression. Theory Proba. Appl. 9 (1964) 141–142. [Google Scholar]
  22. G.S. Watson, Smooth regression analysis. Sankhya 26 (1964) 359–372. [Google Scholar]
  23. G. Wahba, Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial and Applied Mathematics (1990). [Google Scholar]
  24. L. Györfi, M. Kohler, A. Krzyżak and H. Walk, A Distribution-free Theory of Nonparametric Regression. Springer Series in Statistics, Springer-Verlag, New York (2002). [Google Scholar]
  25. R. Horn and C. Johnson, Matrix Analysis, 2nd edn. Cambridge University Press, Cambridge (2013). [Google Scholar]
  26. J.H. Friedman, Stochastic gradient boosting. Computat. Statist. Data Anal. 38 (2002) 367–378. [Google Scholar]
  27. P. Billingsley, Convergence of Probability Measures. Wiley Series in Probability and Statistics: Probability and Statistics, 2nd edn. John Wiley & Sons, Inc., New York (1999). [Google Scholar]
  28. M. Redmond, Communities and Crime. UCI Machine Learning Repository. (2009). [Google Scholar]
  29. T. Apostol, Calculus. Vol. II: Multi-variable Calculus and Linear Algebra, with Applications to Differential Equations and Probability. Blaisdell International Textbook Series. Xerox College Publ. (1969). [Google Scholar]
  30. R. Bellman, Stability Theory of Differential Equations. Dover Books on Intermediate and Advanced Mathematics. Dover Publications (1969). [Google Scholar]
  31. R. Bellman, Introduction to Matrix Analysis: Second Edition. Classics in Applied Mathematics. Society for Industrial and Applied Mathematics (1997). [Google Scholar]
  32. UCI, Machine Learning Repository DOI: [Google Scholar]

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