Anti-concentration for polynomials of independent random variables

Abstract

We prove anti-concentration results for polynomials of independent random variables with arbitrary degree. Our results extend the classical Littlewood-Offord result for linear polynomials, and improve several earlier estimates. We discuss applications in two different areas. In complexity theory, we prove near optimal lower bounds for computing the Parity, addressing a challenge in complexity theory posed by Razborov and Viola, and also address a problem concerning OR functions. In random graph theory, we derive a general anti-concentration result on the number of copies of a fixed graph in a random graph.

Theorem 1.7 on p-biased distribution and Theorem 2.5 on lower bound for approximating OR function are modified due to some errors in the previous version. Theorem 1.8 on general distributions and an application on graph theory are added