- Keywords: Statistical Learning Theory, Concentration Inequalities
- TL;DR: Concentration Inequalities Under Sub-Gaussian and Sub-Exponential Conditions
- Abstract: We prove analogues of the popular bounded difference inequality (also called McDiarmid's inequality) for functions of independent random variables under sub-gaussian and sub-exponential conditions. Applied to vector-valued concentration and the method of Rademacher complexities these inequalities allow an easy extension of uniform convergence results for PCA and linear regression to the case potentially unbounded input- and output variables.
- Supplementary Material: pdf
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