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We present a framework to derive bounds on the test loss of randomized learning algorithms for the case of bounded loss functions.
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A. R. Asadi, E. Abbe, and S. Verdú, “Chaining mutual information and tightening generalization bounds,” in Proc. Conf. Neural Inf. Process. Syst. (NeurIPS) , Montreal, Canada, Dec. 2018
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R. Bassily, S. Moran, I. Nachum, J. Shafer, and A. Yehudayoff, “Learners that use little information,” J. of Mach. Learn. Res. , vol. 83, pp. 25–55, Apr. 2018
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Y. Bu, S. Zou, and V. V. Veeravalli, “Tightening mutual information based bounds on generalization error,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT) , Paris, France, July 2019
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P. Grünwald and N. Mehta, “Fast rates for general unbounded loss functions: from ERM to generalized Bayes,” J. of Mach. Learn. Res. , vol. 83, pp. 1–80, Mar. 2020
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F. Hellström and G. Durisi, “Generalization bounds via information density and conditional information density,” IEEE J. Sel. Areas Inf. Theory , vol. 1, no. 3, pp. 824–839, Dec. 2020
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