Approximating hyper-rectangles: learning and pseudo-random sets
The PAC learning of rectangles has been studied because they have been found experimentally to yield excellent hypotheses for severaf applied learning problems. Also, pseudorandom sets for rectangles have been actively studied recently because (i) they are a subpmblem common to the derandomization of depth-2 (DIW) circuits and derandotnizing Randomized Logspace, and (ii) they approximate the distribution of n independent multivalued random variables. We present improved upper bounds for a class of such problems of “approximating” highdlmensional rectangles that arise in PAC learning and pseudorandomness.