Approximating Hyper-Rectangles: Learning and Pseudo-random Sets

Peter Auer, Philip M. Long, Aravind Srinivasan

Abstract: applied learning problems. Also, pseudorandom sets for rectangles have been actively studied recently because (i) they are a subproblem common to the derandomization of depth-2 (DNF) circuits and derandomizing Randomized Logspace, and (ii) they approximate the distribution of independent multivalued random variables. We present improved upper bounds for a class of such problems of "approximating" high-dimensional rectangles that arise in PAC learning and pseudorandomness.