Let $f$ be analytic on $[0,1]$ with $|f^{(k)}(1/2)|\leq A\alpha^kk!$ for some constant $A$ and $\alpha<2$. We show that the median estimate of $\mu=\int_0^1f(x)\,\mathrm{d}x$ under random linear scrambling with $n=2^m$ points converges at the rate $O(n^{-c\log(n)})$ for any $c< 3\log(2)/\pi^2\approx 0.21$. We also get a super-polynomial convergence rate for the sample median of $2k-1$ random linearly scrambled estimates, when $k=\Omega(m)$. When $f$ has a $p$'th derivative that satisfies a $\lambda$-H\"older condition then the median-of-means has error $O( n^{-(p+\lambda)+\epsilon})$ for any $\epsilon>0$, if $k\to\infty$ as $m\to\infty$
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The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many c...
We develop minimax optimal risk bounds for the general learning task consisting in predicting as wel...
Motivated by two problems on arithmetic progressions (APs)—concerning large deviations for AP count...
We study numerical approximations of integrals [0,1]s f(x) dx by averaging the func-tion at some sam...
We prove that a class of Monte Carlo methods, including averages based on randomized digital nets, L...
AbstractMultivariable trial functions that depend on random parameters are maximized by crude global...
International audienceGerber and Chopin combine SMC with RQMC to accelerate convergence. They apply ...
Random networks of nonlinear functions have a long history of empirical success in function fitting ...
AbstractWe consider approximation of weighted integrals of functions with infinitely many variables ...
The Kaczmarz method, or the algebraic reconstruction technique (ART), is a popular method for solvin...
In recent years, many probabilistic algorithms (i.e., algorithms that can toss coins) that run in po...
The emergence of massive data sets, over the past twenty or so years, has lead to the development of...
We investigate the convergence rate of the perceptron algorithm when the patterns are given with hig...
We analyse the problem of approximating a multivariate function by discrete least-squares projection...
Polynomial chaos (PC) representations for non-Gaussian random variables are infinite series of Hermi...
The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many c...
We develop minimax optimal risk bounds for the general learning task consisting in predicting as wel...
Motivated by two problems on arithmetic progressions (APs)—concerning large deviations for AP count...