  The total influence of a function is a central notion in analysis of Boolean
functions, and characterizing functions that have small total influence is one
of the most fundamental questions associated with it. The KKL theorem and the
Friedgut junta theorem give a strong characterization of such functions
whenever the bound on the total influence is $o(\log n)$. However, both results
become useless when the total influence of the function is $\omega(\log n)$.
The only case in which this logarithmic barrier has been broken for an
interesting class of functions was proved by Bourgain and Kalai, who focused on
functions that are symmetric under large enough subgroups of $S_n$.
  In this paper, we build and improve on the techniques of the Bourgain-Kalai
paper and establish new concentration results on the Fourier spectrum of
Boolean functions with small total influence. Our results include:
  1. A quantitative improvement of the Bourgain--Kalai result regarding the
total influence of functions that are transitively symmetric.
  2. A slightly weaker version of the Fourier--Entropy Conjecture of Friedgut
and Kalai. This weaker version implies in particular that the Fourier spectrum
of a constant variance, Boolean function $f$ is concentrated on $2^{O(I[f]\log
I[f])}$ characters, improving an earlier result of Friedgut. Removing the $\log
I[f]$ factor would essentially resolve the Fourier--Entropy Conjecture, as well
as settle a conjecture of Mansour regarding the Fourier spectrum of polynomial
size DNF formulas.
  Our concentration result has new implications in learning theory: it implies
that the class of functions whose total influence is at most $K$ is
agnostically learnable in time $2^{O(K\log K)}$, using membership queries.
