conference on computer and communications security, pages 308-318, 2016.
[2] Faroudja Abid and Nouma Izeboudjen. Predicting forest fire in algeria using data mining techniques: Case study of the decision tree algorithm. In International Conference on Advanced Intelligent Systems for Sustainable Development, pages 363-370. Springer, 2020.
[3] John M Abowd. The us census bureau adopts differential privacy. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2867-2867, 2018.
[4] Noga Alon, Raef Bassily, and Shay Moran. Limits of private learning with access to public data. Advances in neural information processing systems, 32, 2019.
[5] M Altosole, G Benvenuto, M Figari, and U Campora. Real-time simulation of a cogag naval ship propulsion system. Proceedings of the institution of mechanical engineers, part M: journal of engineering for the maritime environment, 223(1):47-62, 2009.
[6] Brendan Avent, Yatharth Dubey, and Aleksandra Korolova. The power of the hybrid model for mean estimation. Proceedings on Privacy Enhancing Technologies, (4):48-68, 2020.
[7] Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Steven Wu. Private query release assisted by public data. In International Conference on Machine Learning, pages 695–703. PMLR, 2020.
[8] Sergei N. Bernstein. The Theory of Probabilities. Gastehizdat Publishing House, Moscow, 1946.
[9] Thomas B Berrett, László Györfi, and Harro Walk. Strongly universally consistent nonparametric regression and classification with privatised data. *Electronic Journal of Statistics*, 15:2430–2453, 2021.
[10] Alex Bie, Gautam Kamath, and Vikrant Singhal. Private estimation with public data. In Advances in Neural Information Processing Systems, 2022.
[11] L Breiman, JH Friedman, R Olshen, and CJ Stone. Classification and regression trees. 1984.
[12] Thomas F Brooks, D Stuart Pope, and Michael A Marcolini. Airfoil self-noise and prediction. Technical report, 1989.
[13] T Tony Cai and Hongming Pu. Transfer learning for nonparametric regression: Non-asymptotic minimax analysis and adaptive procedure. arXiv preprint arXiv:0000.0000, 2022.
[14] T Tony Cai, Yichen Wang, and Linjun Zhang. The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy. The Annals of Statistics, 49(5):2825-2850, 2021.
[15] Yuchao Cai, Yuheng Ma, Yiwei Dong, and Hanfang Yang. Extrapolated random tree for regression. In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 3442-3468. PMLR, 23-29 Jul 2023.
[16] Chih-Chung Chang and Chih-Jen Lin. Libsvm: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1-27, 2011.
[17] Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Introduction to algorithms. MIT press, 2022.
[18] Paulo Cortez, Antonio Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. Modeling wine preferences by data mining from physicochemical properties. Decision support systems, 47(4):547-553, 2009.
[19] Felipe Cucker and Ding-Xuan Zhou. Learning Theory: An Approximation Theory Viewpoint. Cambridge University Press, 2007.
[20] Fida Kamal Dankar and Khaled El Emam. Practicing differential privacy in health care: A review. Trans. Data Priv., 6(1):35-67, 2013.