Hypothesis Set Stability and Generalization [article]

Dylan J. Foster and Spencer Greenberg and Satyen Kale and Haipeng Luo and Mehryar Mohri and Karthik Sridharan
2020 arXiv   pre-print
We present a study of generalization for data-dependent hypothesis sets. We give a general learning guarantee for data-dependent hypothesis sets based on a notion of transductive Rademacher complexity. Our main result is a generalization bound for data-dependent hypothesis sets expressed in terms of a notion of hypothesis set stability and a notion of Rademacher complexity for data-dependent hypothesis sets that we introduce. This bound admits as special cases both standard Rademacher
more » ... bounds and algorithm-dependent uniform stability bounds. We also illustrate the use of these learning bounds in the analysis of several scenarios.
arXiv:1904.04755v3 fatcat:q5g4q7mwnjdpllkhtbdu2lvgga