Optimal Bounds on the VC-dimension
This provides foundational theoretical insights for machine learning and geometry communities, addressing core complexity measures.
The paper resolves two longstanding open problems by establishing optimal bounds on the VC-dimension for k-fold unions/intersections of half-spaces and simplices set systems, settling a question first studied in 1989.
The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: $k$-fold unions/intersections of half-spaces, and the simplices set system. Among other implications, it settles an open question in machine learning that was first studied in the 1989 foundational paper of Blumer, Ehrenfeucht, Haussler and Warmuth as well as by Eisenstat and Angluin and Johnson.