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Kevin Tanguy

Publications and source records attributed to Kevin Tanguy.

4 recordsLinked to original sources

Talagrand inequality at second order and application to Boolean analysis

This note is concerned with an extension, at second order, of an inequality on the discrete cube $C_n=\{-1,1\}$ (equipped with the uniform measure) due to Talagrand (\cite{TalL1L2}). As an application, the main result of this note is a Theorem in the spirit of a famous result from Kahn, Kalai and Linial (cf. \cite{KKL}) concerning the influence of Boolean functions. The notion of the influence of a couple of coordinates $(i,j)\in\{1,\ldots,n\}^2$ is introduced in section 2 and the following alternative is obtained : for any Boolean function $f\,:\, C_n\to \{0,1\}$, either there exists a coordinate with influence at least of order $(1/n)^{1/(1+η)}$, with $\, 0<η<1$ (independent of $f$ and $n$) or there exists a couple of coordinates $(i,j)\in\{1,\ldots,n\}^2$ with $i\neq j$, with influence at least of order $(\log n/n)^2$. In section 4, it is shown that this extension of Talagrand inequality can also be obtained, with minor modifications, for the standard Gaussian measure $γ_n$ on $\mathbb{R}^n$ ; the obtained inequality can be of independent interest. The arguments rely on interpolation methods by semigroup together with hypercontractive estimates. At the end of the article, some related open questions are presented.

math.PR

Non-asymptotic variance bounds and deviation inequalities by optimal transport

The purpose of this note is to show how simple Optimal Transport arguments, on the real line, can be used in Superconcentration theory. This methodology is efficient to produce sharp non-asymptotic variance bounds for various functionals (maximum, median, $l^p$ norms) of standard Gaussian random vectors in $\R^n$. The flexibility of this approach can also provide exponential deviation inequalities reflecting preceding variance bounds. As a further illustration, usual laws from Extreme theory and Coulomb gases are studied.

math.PR

Some superconcentration inequalities for extrema of stationary Gaussian Processes

This note is concerned with concentration inequalities for extrema of stationary Gaussian processes. It provides non-asymptotic tail inequalities which fully reflect the fluctuation rate, and as such improve upon standard Gaussian concentration. The arguments rely on the hypercontractive approach developed by Chatterjee for superconcentration variance bounds. Some statistical illustrations complete the exposition.

math.PR