Gabriel Romon
Interested in theoretical aspects of Statistics and Machine Learning. Studied at ENSAE Paris and ENS ParisSaclay, got a master's degree from the latter (MVA).
Currently a visiting Research Intern at Rutgers University.
A few recent good answers of mine:
A concentration bound for the squared norm of an isotropic Gaussian vector
$X\in[a,b]$ and $E(X)=0$ imply $E(X^2)\leq ab$
$(XY)\in L^2(P) \implies X,Y\in L^1(P)$
DCT for convergence in probability
$\frac{S_n}{\sqrt n}$ is dense in $\mathbb R$ almost surely
Showing $(X_n >c_n \text{ i.o.})=(\max_{1\leq i\leq n}X_i >c_n \text{ i.o.})$
Derivative of the MGF
Infinite convex combination of characteristic functions is a characteristic function
Different $\mathcal C^\infty$ characteristic functions that coincide in a neighborhood of $0$
Different metrics that metrize convergence in probability
Relations between different definitions of the Gaussian width
Weak consistency from asymptotic unbiasedness
$(\sum_{j=1}^{n} X_{j}) / b_{n} \overset {P}{\to} C$ implies $b_{n}\sim b_{n+1}$
CLT and pointwise convergence of densities
If $X\in L^1$, $P(X>x)=o\left(\frac 1x\right)$
Convex function with directional derivatives in all directions is differentiable
Concentration of the $q$norm of a Gaussian vector
Almost sure convergence of $\sum_n \frac{X_n}n$

Piscataway, NJ, USA

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Last seen Jan 24 at 0:43
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