DYOGENE/ERC NEMO 2023 : Seminar series

We consider homogeneous real random functions defined on the Euclidean space.

We study approximation problems in reproducing kernel Hilbert spaces (RKHS) using random nodes.

The work is motivated by car-sharing systems like Autolib’ in Paris (2011-2017) where, for charging, electric cars are parked in small capacity nodes.

We introduce spatial regulation properties for stationary spatial point processes and develop...

A Hawkes process on the real line is a point process whose intensity function at time is a functional of its past activity before time.

We present some Poisson process approximation results for stabilizing functionals of Poisson (or Binomial) processes that arise in stochastic geometry.

Local weak convergence of graphs, introduced by Itaï Benjamini and Oded Schramm in 2001, tries to understand the intern geometry of a typical vertex in large graphs...

It’s a situation everyone dreads. A road is down to one lane for repairs.

We discuss how these models arise from random directed graphs and talk about perfect simulation.

The Dirichlet-Ferguson measure is a cornerstone in Bayesian nonparametrics...

The von Neumann-Day problem asks whether every non-amenable group contains a non-abelian free group.

Linear programming, and more generally convex semialgebraic programming, makes sense...

Regularization is a widespread technique used in statistical estimation problems that helps to capture low dimensional structure in the data and improve signal recovery.

The uniform measure on the set of all spanning trees of a finite graph is a classical object in probability.

A Markov decision process is called reversible if for every stationary Markov control strategy the resulting Markov chain is reversible.

It is known that the size of the largest common subtree...

Consider two binary trees whose leaves are labelled from 1 to n.

The standard setting for studying parallel server systems (PSS) at the diffusion scale is based on the heavy traffic condition (HTC)...

In this talk I will briefly recall the framework of local weak limits of finite graphs introduced by I. Benjamini and O. Schramm

Motivated by Krioukov et al.'s model of random hyperbolic graphs for real-world networks, and inspired by the analysis of a dynamic model of graphs in Euclidean space by Peres et al., we introduce a

Combinatorial maps are a model of discrete geometry: they are surfaces made by gluing  polygons along their sides, or equivalently, graphs drawn on surfaces. In this talk, I'll focus on the study of

A matching in a graph is a set of edges that do not share endpoints. Developing algorithms that find large matchings is an important problem. An algorithm is said to be online if it has to construct

We consider a sequence of Poisson cluster point processes on $R^d$: At step $n\in\mathbb{N}_0$ of the construction, the cluster centers have intensity $c/(n+1)$ for some $c > 0$, and each cluster

Cost is a natural invariant associated to group actions and invariant point processes on symmetric spaces (such as Euclidean space and hyperbolic space). Informally, it measures how difficult it is to