Some Hoeffding- and Bernstein-type Concentration Inequalities

This work addresses theoretical foundations in machine learning by providing tools for analyzing generalization bounds, but it is incremental as it builds on existing concentration inequality frameworks.

The paper tackles the problem of deriving concentration inequalities for functions of independent random variables under sub-gaussian and sub-exponential conditions, resulting in an extension of the Rademacher complexities method to Lipschitz function classes and unbounded sub-exponential distributions.