List Sample Compression and Uniform Convergence

arXiv:2403.10889v1 Announce Type: new
Abstract: List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list learning. Our primary goal is to determine whether classical principles in the PAC setting retain their applicability in the domain of list PAC learning. We focus on uniform convergence (which is the basis of Empirical Risk Minimization) and on sample compression …

abstract arxiv classification compression context convergence cs.lg instance labels list multiple sample stat.ml type uniform