A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Functions of matrices have been studied for as long as matrix algebra itself. Indeed, in his
seminal A Memoir on the Theory of Matrices (1858), Cayley investigated the square root of a …

This book offers a comprehensive introduction to the theory of linear and nonlinear Volterra
integral equations (VIEs), ranging from Volterra's fundamental contributions and the …

We study the stability of quasinormal modes (QNM) in asymptotically flat black hole
spacetimes by means of a pseudospectrum analysis. The construction of the Schwarzschild …

This book is the written and somewhat expanded version of the author's lectures delivered at
the NSF-CBMS international research conference organized at the Mathematics Department …

Applying CNNs to extrinsic surface representation such as volumetric grids [Qi et al. 2016b]
or depth map projections on extrinsic 2D cameras [Wei et al. 2016] requires working with 3D …

We provide the rigorous derivation of the wave kinetic equation from the cubic nonlinear
Schrödinger (NLS) equation at the kinetic timescale, under a particular scaling law that …

Black hole spectroscopy is a powerful tool to probe the Kerr nature of astrophysical compact
objects and their environment. The observation of multiple ringdown modes in gravitational …

The aim of this third edition is to give an accessible and essentially self-contained account of
pseudo-differential operators based on the previous edition. New chapters notwithstanding …

We analyze to what extent the complex Langevin method, which is in principle capable of
solving the so-called sign problems, can be considered as reliable. We give a formal …