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Continuous LWE
We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE.
On Learning Mixtures of Well-Separated Gaussians
In the most basic form of this problem, we are given samples from a uniform mixture of $k$ standard spherical Gaussians, and the goal is to estimate the means up to accuracy $\delta$ using $poly(k, d, 1/\delta)$ samples.
An Inequality for Gaussians on Lattices
$ \newcommand{\R}{\ensuremath{\mathbb{R}}} \newcommand{\lat}{\mathcal{L}} \newcommand{\ensuremath}[1]{#1} $We show that for any lattice $\lat \subseteq \R^n$ and vectors $\vec{x}, \vec{y} \in \R^n$, \[ \rho(\lat + \vec{x})^2 \rho(\lat + \vec{y})^2 \leq \rho(\lat)^2 \rho(\lat + \vec{x} + \vec{y}) \rho(\lat + \vec{x} - \vec{y}) \; , \] where $\rho$ is the Gaussian measure $\rho(A) := \sum_{\vec{w} \in A} \exp(-\pi \| \vec{w} \|^2)$.
Probability Functional Analysis Number Theory
