5 citations · 6 across the 2 of their papers we have counts for
5 papers · 1 filter
Non-asymptotic approximations of neural networks by Gaussian processes
Ronen Eldan, Dan Mikulincer, Tselil Schramm
We study the extent to which wide neural networks may be approximated by Gaussian processes when initialized with random weights. It is a well-established fact that as the width of…
Stability estimates for invariant measures of diffusion processes, with applications to stability of moment measures and Stein kernels
Max Fathi, Dan Mikulincer
We investigate stability of invariant measures of diffusion processes with respect to distances on the coefficients, under an assumption of log-concavity. The method is a var…
A CLT in Stein's distance for generalized Wishart matrices and higher order tensors
Dan Mikulincer
We study the central limit theorem for sums of independent tensor powers, . We focus on the high-dimensional regime where $X_…
Stability of Talagrand's Gaussian transport-entropy inequality via the Föllmer process
Dan Mikulincer
We establish a dimension-free improvement of Talagrand's Gaussian transport-entropy inequality, under the assumption that the measures satisfy a Poincaré inequality. We also study…
The CLT in high dimensions: quantitative bounds via martingale embedding
Ronen Eldan, Dan Mikulincer, Alex Zhai
We introduce a new method for obtaining quantitative convergence rates for the central limit theorem (CLT) in a high dimensional setting. Using our method, we obtain several new bo…