On the Fourier Entropy Influence Conjecture for Extremal Classes

The Fourier Entropy-Influence (FEI) Conjecture of Friedgut and Kalai states that ${\bf H}[f] \leq C \cdot {\bf I}[f]$ holds for every Boolean function $f$, where ${\bf H}[f]$ denotes the spectral entropy of $f$, ${\bf I}[f]$ is its total influence, and $C > 0$ is a universal constant. Despite significant interest in the conjecture it has only been shown to hold for some classes of Boolean functions such as symmetric functions and read-once formulas. In this work, we prove the conjecture for extremal cases, functions with small influence and functions with high entropy. Specifically, we show that: * FEI holds for the class of functions with ${\bf I}[f] \leq 2^{-cn}$ with the constant $C = 4 \cdot \frac{c+1}{c}$. Furthermore, proving FEI for a class of functions with ${\bf I}[f] \leq 2^{-s(n)}$ for some $s(n) = o(n)$ will imply FEI for the class of all Boolean functions. * FEI holds for the class of functions with ${\bf H}[f] \geq cn$ with the constant $C = \frac{1 + c}{h^{-1}(c^2)}$. Furthermore, proving FEI for a class of functions with ${\bf H}[f] \geq s(n)$ for some $s(n) = o(n)$ will imply FEI for the class of all Boolean functions. Additionally, we show that FEI holds for the class of functions with constant $\|\widehat{f}\|_1$, completing the results of Chakhraborty et al. that bounded the entropy of such functions. We also improve the result of Wan et al. for read-k decision trees, from ${\bf H}[f] \leq O(k) \cdot {\bf I}[f]$ to ${\bf H}[f] \leq O(\sqrt{k}) \cdot {\bf I}[f]$. Finally, we suggest a direction for proving FEI for read-k DNFs, and prove the Fourier Min-Entropy/Influence (FMEI) Conjecture for regular read-k DNFs.

[1]  J. Bourgain,et al.  Influences of Variables and Threshold Intervals under Group Symmetries , 1997 .

[2]  Yishay Mansour,et al.  An O(n^(log log n)) Learning Algorithm for DNT under the Uniform Distribution , 1995, J. Comput. Syst. Sci..

[3]  Ryan O'Donnell,et al.  A Composition Theorem for the Fourier Entropy-Influence Conjecture , 2013, ICALP.

[4]  Satyanarayana V. Lokam,et al.  Upper bounds on Fourier entropy , 2015, Theor. Comput. Sci..

[5]  Elchanan Mossel,et al.  A note on the Entropy/Influence conjecture , 2012, Discret. Math..

[6]  Bireswar Das,et al.  The Entropy Influence Conjecture Revisited , 2011, Electron. Colloquium Comput. Complex..

[7]  Avishay Tal,et al.  Degree and Sensitivity: tails of two distributions , 2016, Electron. Colloquium Comput. Complex..

[8]  T. Sanders,et al.  Analysis of Boolean Functions , 2012, ArXiv.

[9]  Andrew Wan,et al.  Decision trees, protocols and the entropy-influence conjecture , 2014, ITCS.

[10]  G. Kalai,et al.  Every monotone graph property has a sharp threshold , 1996 .

[11]  Yishay Mansour,et al.  Weakly learning DNF and characterizing statistical query learning using Fourier analysis , 1994, STOC '94.

[12]  Nathan Linial,et al.  The influence of variables on Boolean functions , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[13]  Adam Tauman Kalai,et al.  Agnostically learning decision trees , 2008, STOC.

[14]  Ryan O'Donnell,et al.  The Fourier Entropy-Influence Conjecture for Certain Classes of Boolean Functions , 2011, ICALP.

[15]  Andrew Wan,et al.  Mansour's Conjecture is True for Random DNF Formulas , 2010, COLT.