Talagrand's discrete-cube anti-concentration conjecture

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Let n≥1n\geq 1 and t≥0t\geq 0, let μt=1−e−t2δ−1+1+e−t2δ1\mu_t=\frac{1-e^{-t}}{2}\delta_{-1}+\frac{1+e^{-t}}{2}\delta_1, and let μ=μ∞\mu=\mu_\infty be the uniform probability measure on {−1,1}\{-1,1\}. Write μn\mu^n for the product measure and let Ttf=f∗μtnT_t f=f*\mu_t^n on functions f:{−1,1}n→Rf:\{-1,1\}^n\to\mathbb R. For non-negative ff, write ∥f∥1\|f\|_1 for its L1(μn)L^1(\mu^n) norm. Talagrand's conjecture. For every t>0t>0, there exists a function φt:[1,∞)→[1,∞)\varphi_t:[1,\infty)\to[1,\infty) with

lim⁡α→∞φt(α)=∞\lim_{\alpha\to\infty}\varphi_t(\alpha)=\infty

such that for every f:{−1,1}n→R+f:\{-1,1\}^n\to\mathbb R_+ and every α>1\alpha>1,

μn({x∈{−1,1}n:Ttf(x)>α∥f∥1})≤1αφt(α).\mu^n\left(\{x\in\{-1,1\}^n:T_tf(x)>\alpha\|f\|_1\}\right)\leq\frac{1}{\alpha\varphi_t(\alpha)}.

This is presented as a discrete-cube generalization of the Gaussian conjecture and is proved by approximately embedding Gaussian space into discrete cubes of growing dimension via the central limit theorem.

References

Primary source

Ronen Eldan and James R. Lee, “Regularization under diffusion and anti-concentration of the information content”, arXiv:1410.3887 (2017).

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