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Given a Boolean function $f:{-1,1}^nto {-1,1}$, the Fourier distribution assigns probability $widehat{f}(S)^2$ to $Ssubseteq [n]$. The Fourier Entropy-Influence (FEI) conjecture of Friedgut and Kalai asks if there exist a universal constant C>0 such that $H(hat{f}^2)leq C Inf(f)$, where $H(hat{f}^2)$ is the Shannon entropy of the Fourier distribution of $f$ and $Inf(f)$ is the total influence of $f$. 1) We consider the weaker Fourier Min-entropy-Influence (FMEI) conjecture. This asks if $H_{infty}(hat{f}^2)leq C Inf(f)$, where $H_{infty}(hat{f}^2)$ is the min-entropy of the Fourier distribution. We show $H_{infty}(hat{f}^2)leq 2C_{min}^oplus(f)$, where $C_{min}^oplus(f)$ is the minimum parity certificate complexity of $f$. We also show that for every $epsilongeq 0$, we have $H_{infty}(hat{f}^2)leq 2log (|hat{f}|_{1,epsilon}/(1-epsilon))$, where $|hat{f}|_{1,epsilon}$ is the approximate spectral norm of $f$. As a corollary, we verify the FMEI conjecture for the class of read-$k$ $DNF$s (for constant $k$). 2) We show that $H(hat{f}^2)leq 2 aUC^oplus(f)$, where $aUC^oplus(f)$ is the average unambiguous parity certificate complexity of $f$. This improves upon Chakraborty et al. An important consequence of the FEI conjecture is the long-standing Mansours conjecture. We show that a weaker version of FEI already implies Mansours conjecture: is $H(hat{f}^2)leq C min{C^0(f),C^1(f)}$?, where $C^0(f), C^1(f)$ are the 0- and 1-certificate complexities of $f$, respectively. 3) We study what FEI implies about the structure of polynomials that 1/3-approximate a Boolean function. We pose a conjecture (which is implied by FEI): no flat degree-$d$ polynomial of sparsity $2^{omega(d)}$ can 1/3-approximate a Boolean function. We prove this conjecture unconditionally for a particular class of polynomials.
The Fourier-Entropy Influence (FEI) Conjecture states that for any Boolean function $f:{+1,-1}^n to {+1,-1}$, the Fourier entropy of $f$ is at most its influence up to a universal constant factor. While the FEI conjecture has been proved for many cla
In 1992 Mansour proved that every size-$s$ DNF formula is Fourier-concentrated on $s^{O(loglog s)}$ coefficients. We improve this to $s^{O(loglog k)}$ where $k$ is the read number of the DNF. Since $k$ is always at most $s$, our bound matches Mansour
Feature selection, in the context of machine learning, is the process of separating the highly predictive feature from those that might be irrelevant or redundant. Information theory has been recognized as a useful concept for this task, as the predi
We present generalized methods for calculating lower bounds on the ground-state entropy per site, $S_0$, or equivalently, the ground-state degeneracy per site, $W=e^{S_0/k_B}$, of the antiferromagnetic Potts model. We use these methods to derive improved lower bounds on $W$ for several lattices.
We show that the conditional min-entropy Hmin(A|B) of a bipartite state rho_AB is directly related to the maximum achievable overlap with a maximally entangled state if only local actions on the B-part of rho_AB are allowed. In the special case where