Workshop: Rough paths and related topics
Tuesday, November 10, 2026
Milano, Room MM, Via Mangiagalli 37.
Schedule:
14h-15h Lorenzo Zambotti (University of Sorbonne), Product of differential forms with distributional coefficients
15h-16h Gianmario Tessitore ( University of Milano Bicocca ), Sewing and Regularity gains for convolution integrals (joint work with Davide Addona and Luca Lorenzi).
Lorenzo Zambotti
Abstract: We discuss how Hairer’s reconstruction theorem allows to define a canonical product of differential forms with distributional coefficients, in a regularity regime which would be inaccessible for standard distributions.
Gianmario Tessitore
Abstract: We study convolution integrals driven by Hölder and rough signals in the presence of a smoothing semigroup. A semigroup-adapted sewing procedure allows us to quantify their gain in spatial regularity. We then extend the construction to singular integrands, which may blow up near the initial time, by means of weighted Hölder estimates.
The resulting bounds are applied to mild Young equations with irregular initial data.
We conclude with perspectives involving stochastic sewing and rough factorisation arguments.
The workshop is organized by Francesco De Vecchi (Università di Pavia), Frédéric Patras (CNRS-Université Cote d’Azur) e Stefania Ugolini (Università di Milano) as the third scientific event of the Intensive Research Trimester titled: "Algebraic & Geometric Structures in Stochastic Calculus" of the Center for Training and Research, in the framework of the Excellence Project 2023-2027 of the Department of Mathematics " “F. Enriques”, Milano.
https://agssc.unimi.it/
Dear all,
On Monday, November 9, at 14h00 in Aula Dal Passo in the Math Department of the University of Rome Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/events.php) will host
Lucia Celli (University of Luxembourg) with the seminar
“Quantitative CLTs for neural network at random initialization”
Abstract: New quantitative bounds for the convergence of wide feed-forward neural networks to Gaussian processes are presented. For fully connected networks with i.i.d. weights and Gaussian biases, convergence at rate $O(n^{-1/2})$ is established in the Kolmogorov and Wasserstein distances when the network is evaluated at a single input, and in the convex distance when it is evaluated at multiple inputs, as the hidden-layer width $n \to \infty$. The bounds are explicit in the hyperparameters of the network and they give us the possibility to study the infinite depth-width limit. The main tool is Stein's method, following the approach of Chatterjee (2008) and subsequent extensions by Kasprzak, Peccati (2022) and Lachieze-Rey, Peccati (2017). The talk will focus on the proof of the Kolmogorov distance bound, where a key step consists in solving a Stein equation associated with the square of a Lipschitz function.
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/meet/31063852152728?p=gXh2dasNv6Msd83RWS
The seminar is part of the Excellence Project MatMod@TOV.
Sono stati pubblicati i bandi GNAMPA per le seguenti attività:
- Professori Visitatori
- Organizzazione di Convegni, Scuole, Workshop e Seminari
- Progetti di Ricerca 2027
La scadenza per presentare le domande è il 16 dicembre 2026.
I bandi e le modalità di presentazione delle domande sono disponibili alla pagina seguente:
https://www.altamatematica.it/gnampa/2026/10/29/prossime-scadenze/
Cordiali saluti,
FC
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Francesco Caravenna
Dipartimento di Matematica e Applicazioni
Università degli Studi di Milano-Bicocca
https://fcaraven.github.io/
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