In the coming Thursday seminar, we will go through the paper Poly-logarithmic independence fools $AC^0$ circuits.

An $AC^0$ circuit is a Boolean circuit containing NOT, AND, OR gates. We count the number of AND, OR gates as the \emph{size} of the circuit. While the \emph{depth} of a circuit is defined as the maximum number of AND, OR gates on any path from an input to the output.

We say a distribution $\mu$ fools a circuit $f$, if $|\mathbf{E}_{x \sim \mu}[f(x)] - \mathbf{E}_{x}[f(x)]| < \epsilon$. I.e., the expected output of $f$ on $\mu$ is ``roughly'' the same as that on uniform distribution.

The problem considered in this paper is on the power of $r$-independence to fool $AC^0$ circuits. Given $\epsilon$ as a constant, how large has $r$ to be so that any $r$-wise independent distribution fools all the $AC^0$ circuit of size $m$ and a constant depth $d$? The main theorem in the paper shows that $r$ is enough to be poly-logarithmic with $m$. In fact, the author proves that $r = (\log (m / \epsilon))^{O(d^2)}$ suffices. And this resolves a conjecture by [Linial-Nisan'90], which conjectured $r = O((\log m))^{d - 1}$ is enough, up to a gap between $O(d)$ and $O(d^2)$ in the exponent. The only prior progress on this problem was by [Bazzi'07], which showed that $O(\log^2 m)$-wise independent distributions fool depth-2 circuits (DNF/CNF).

The (very rough) approach to prove is to find a low degree polynomial $g$ which approximates the expectation of $f$ under both uniform distribution and a given $r$-wise independent distribution. This $g$ would set up a bridge from $\mathbf{E}_{\mu}[f]$ to $\mathbf{E}[f]$ by $\mathbf{E}[f] \approx \mathbf{E}[g] \approx \mathbf{E}_{\mu}[g] \approx \mathbf{E}_{\mu}[f]$. But since this $g$ seems hard to find (maybe it's hard because it needs to be specific for a specific distribution), the proof in the paper actually approximates $f$ by low-degree functions $g$ in the following two senses.

(1) [Beigel-Reingold-Spielman'91, Tarui'93] Find low-degree (but unbounded) $g$ so that $\mathbf{Pr}[f \neq g] < \epsilon, \mathbf{Pr} _{\mu}[f \neq g] < \epsilon$.

(2) [Linial-Mansour-Nisan'93] Find low-degree $g$ so that $\| f - g \|_2^2 < \epsilon$.

The proof succeeds by the two results above as building blocks and some other clever observations, all of which we will go through in the class.

The proof of [Linial-Mansour-Nisan'93] theorem also uses Fourier analytic tools. But no background on these will be needed -- I will introduce them in the class.

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