Random January 2024

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Concorso da RTDb di MAT/06 a Padova - scadenza 25/8/2016
by Marco Ferrante 25 Jun '24

25 Jun '24

Cari colleghi, vi segnalo che il Dipartimento di Matematica dell'Università di Padova ha bandito un posto di ricercatore di tipo B nel settore MAT/06. Il bando è disponibile alla pagina http://www.math.unipd.it/it/news/?id=1898 La scadenza per la presentazione delle domande è il 25 agosto 2016. Marco Ferrante

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Seminar: Adriano Barra at Tor Vergata 02.11
by Michele Salvi 22 Mar '24

22 Mar '24

Dear all, On Thursday, November 2nd, at 15h00 in Aula Dal Passo of Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Adriano Barra (Università del Salento) with the seminar "A walk in the statistical mechanics of neural networks” Abstract: Purpose of this talk is twofold: in the first part, I will provide a statistical mechanical picture of shallow networks proving how learning in biological neural networks (e.g. in the Hopfield model) and standard machine learning via gradient descent (e.g. on the Boltzmann machine) ultimately convey the same information after training. This diminishes the gap in our understanding between biological and artificial information processing networks. In the second part of the talk, focusing on a toy model for the sake of simplicity, I will show how recent mathematical techniques, heavily based on Guerra's interpolation, are suitable to describe the emergent properties of this kind of networks. We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw… <https://teams.microsoft.com/l/meetup-join/19:rfsL73KX-fw86y1YnXq2nk5VnZFwPU…> The seminar is part of the Excellence Project MatMod@TOV.

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Seminar: Marco Carfagnini at Tor Vergata 25.09
by Michele Salvi 08 Mar '24

08 Mar '24

Dear all, We are happy to announce that on Monday, September 25th, at 15h00 in Aula Dal Passo of Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Marco Carfagnini (University of California San Diego) with the seminar "Spectral gaps via small deviations” Abstract: In this talk we will discuss spectral gaps of second order differential operators and their connection to limit laws such as small deviations and Chung’s laws of the iterated logarithm. The main focus is on hypoelliptic diffusions such as the Kolmogorov diffusion and horizontal Brownian motions on Carnot groups. If time permits, we will discuss spectral properties and existence of spectral gaps on general Dirichlet metric measure spaces.This talk is based on joint works with Maria (Masha) Gordina and Alexander (Sasha) Teplyaev. We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw…"Tid"%3a"24c5be2a-d764-40c5-9975-82d08ae47d0e"%2c"Oid"%3a"650fc4a8-4cec-4bd2-87bc-90d134074fe6”} <https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw…> . The seminar is part of the Excellence Project MatMod@TOV.

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Winter mini-course on machine learning at Tor Vergata
by Michele Salvi 13 Feb '24

13 Feb '24

Dear all, Also this Winter RoMaDS ( https://www.mat.uniroma2.it/~rds/events.php ) will offer a mini-course on the mathematics of machine learning at Tor Vergata University in Rome. This year we are very happy to host Giovanni Conforti (Università di Padova & École Polytechnique, Paris) and Alain Durmus (École Polytechnique, Paris) from the 19th to the 22nd of February. They will present a 9 hours mini-course titled An introduction to Score-based Generative Models The course will be addressed also to non-experts of the field, it will be divided in 3 slots of 3 hours each and the tentative program is as follows (see the whole abstract at the end of the email): Lecture 1 & Lecture 2. (Mon 19th, 14h00-17h00) Introduction to generative models & Probabilistic/statistical tools for generative models Lecture 3 & Lecture 4. (Wed 21st, 09h30-12h30) A ”discrete” introduction to score-based diffusion models & A Itô-diffusion viewpoint on score-based diffusion models. Lecture 5 & Lecture 6. (Thu 22nd, 09h30-12h30) Convergence bounds for score-based diffusion models via stochastic control & Optimal transport and entropic optimal transport for generative modeling. You can find the whole Syllabus with more details on each lecture on RoMaDS webpage: https://www.mat.uniroma2.it/~rds/Files/SyllabusSGM.pdf All the lectures will be held in Aula Dal Passo, Department of Mathematics - Tor Vergata University. We do encourage in-person participation. Should you unable to come, here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFw… The seminar is part of the Excellence Project MatMod@TOV. Best, Michele Salvi Abstract of the course “An introduction to Score-based Generative Models” In simple words, generative modeling consists in learning a map capable of generating new data instances that resemble a given set of observations, starting from a simple prior distribution, most often a standard Gaussian distribution. This course aims at providing a mathematical introduction to generative models and in particular to Score-based Generative Models (SGM). SGMs have gained prominence for their ability to generate realistic data across diverse domains, making them a popular tool for researchers and practitioners in machine learning. Participants will learn about the methodological and theoretical foundations, as well as some practical applications associated with these models. The first two lectures motivate the use of generative models, introduce their formalism and present two simple though relevant examples: energy-based models and Generative Adversarial Networks. In the third and fourth lecture we present score-based diffusion models and explain how they provide an algorithmical framework to the basic idea that sampling from the time-reversal of a diffusion process converts noise into new data instances. We shall do so following two different approaches: a first elementary one that only relies on discrete transition probabilities, and a second one based on stochastic calculus. After this introduction, we derive sharp theoretical guarantees of convergence for score-based diffusion models assembling together ideas coming from stochastic control, functional inequalities and regularity theory for HamiltonJacobi-Bellman equations. The course ends with an overview of some of the most recent and sophisticated algorithms such as flow matching and diffusion Schrödinger bridges (DSB), which bring an (entropic) optimal transport insight into generative modeling.

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Deadline extensions WQF2024, Bologna
by Giacomo Bormetti 31 Jan '24

31 Jan '24

Dear Colleagues, We are pleased to inform you of the following deadline extensions for WQF2024. Important dates: 1. Extended Abstract Submission: until January 31, 2024, February 11, 2024, 11:59 PM. 2. Notification of Acceptance: February 21, 2024, March 3, 2024. 3. Full Paper Submission of Accepted Contributions: until March 21, 2024. 4. Registration of Participants: until March 21, 2024. Registration is free but mandatory. A link for registration will be available from the website starting on March 3, 2024. We are looking forward to meeting you in Bologna. Giacomo Bormetti On behalf of the Organizing Committee https://eventi.unibo.it/qfw2024

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Seminario Leonardo Lelli
by De Santis, Emilio 30 Jan '24

30 Jan '24

Cari, vi scrivo per annunciare il seminario di Leonardo Lelli il 2 febbraio 2024 alle ore 14 in Sala di Consiglio presso il Dipartimento di Matematica Guido Castelnuovo, Sapinza Università di Roma, Piazzale Aldo Moro, 5 Roma ............................................................................................................. Title Zero-temperature stochastic Ising model on quasi-transitive graphs Abstract We examine the question of fixation for zero-temperature stochastic Ising model on some connected quasi-transitive graphs. The initial spin configuration is distributed according to a Bernoulli product measure with parameter $p\in (0,1) $. We prove that the shrink property for the underlying graph is a necessary condition for all sites to flip infinitely often almost surely (in this case the model is said of type I). Our main result shows that if $p=1/2 $ and the graph is connected and invariant under rotations and translations, then a strengthening of the shrink property, called the planar shrink property, implies that the model is of type I. We provide an infinite class of graphs having the planar shrink property. Finally, we prove that for one-dimensional translation invariant graphs, the shrink property is a necessary and sufficient condition for the model to be of type I. This talk is based on joint work with my thesis advisor Emilio De Santis. ............................................................................................. Cordiali saluti, Emilio De Santis

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Nuovi Seminari PRISMA - 5 feb 2024
by Francesco Caravenna 30 Jan '24

30 Jan '24

Nuovi Seminari PRISMA 2024 Annunciamo con piacere il nuovo programma di seminari on-line del gruppo UMI - PRISMA (PRobability In Statistics, Mathematics and Applications: http://www.umi-prisma.polito.it/). Da quest'anno i seminari avranno un formato diverso, in uno stile di "colloquium", per creare un'occasione di scambio e discussione con tutta la comunità dei probabilisti e statistici italiani. Ogni giornata comprenderà due relatori, che terranno due seminari di 30 minuti strettamente connessi, per presentare alla comunità una prospettiva sul proprio ambito di ricerca. Il primo appuntamento si terrà lunedì 5 febbraio 2024. I relatori saranno Sandra Fortini (Università Bocconi) e Dario Trevisan (Università di Pisa) che presenteranno Un'introduzione ai limiti di scala delle reti neurali artificiali con il seguente orario: 16:00 Primo seminario 16:30 Pausa e discussione 16:45 Secondo seminario 17:15 Conclusione e discussione Trovate di seguito il riassunto. I seminari verranno trasmessi via Zoom al seguente link: https://uniroma1.zoom.us/j/82939128330 <https://uniroma1.zoom.us/j/82939128330> Meeting ID: 829 3912 8330 Vi aspettiamo numerosi! Valentina Cammarota e Francesco Caravenna %%%%%%%%%%%%%%%%%%%%%%%%%%%% RELATORI: Sandra Fortini (Università Bocconi), Dario Trevisan (Università di Pisa) TITOLO: Un'introduzione ai limiti di scala delle reti neurali artificiali RIASSUNTO: Il seminario si propone di presentare un'introduzione alla teoria dei limiti di scala delle reti neurali artificiali, con un'attenzione particolare rivolta a un pubblico esperto di probabilità e statistica matematica. La tendenza attuale nel campo dell'apprendimento automatico è di utilizzare reti neurali profonde con un numero enorme di parametri, inizializzati spesso in modo (pseudo-)casuale. Questo approccio, che nella pratica si rivela estremamente efficace, offre anche una prospettiva teorica utile per comprendere alcune delle proprietà che rendono tale tecnica così potente, ma anche di evidenziarne possibili limiti. L'intervento sarà suddiviso in due parti, ciascuna incentrata su un aspetto del tema. Dopo una breve introduzione alle reti neurali artificiali e al loro utilizzo pratico nell'apprendimento automatico, la prima parte si concentrerà sulla struttura delle reti all'inizializzazione, limitandoci per semplicità alle architetture più semplici. Esploreremo in più dettaglio alcuni dei risultati principali riguardanti i limiti di scala ``ampio'' e ``profondo'' per reti neurali inizializzate con pesi casuali, concentrandoci su alcuni risultati recenti che stabiliscono una equivalenza asintotica tra reti neural profonde e opportuni processi gaussiani. Nella seconda parte, ci concentreremo sulla dinamica dell'addestramento delle reti neurali. Dopo una breve panoramica sui principali algoritmi utilizzati in scenari reali, ci concentreremo su alcuni dei risultati teorici rigorosi, validi per reti ampie e/o profonde: descriveremo in particolare la dinamica guidata dal Neural Tangent Kernel (NTK) nel limite dell'addestramento ``lazy'' e la dinamica di gradiente di Wasserstein nel limite di campo medio. %%%%%%%%%%%%%%%%%%%%%%%%%%%%

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6 feb 2024 - Seminario Dr. Luca Gonzato
by Enea Bongiorno 30 Jan '24

30 Jan '24

Carissimi colleghi, scusandomi per eventuali invii multipli, vi inoltro il seguente annuncio di seminario. Tutti gli interessati sono invitati a partecipare. Cordialmente, Enea Bongiorno -------------------------------- 6 febbraio 2024 ore 14.00 - aula 206, campus Perrone, via Perrone, 18, Novara. Università del Piemonte Orientale. - on-line: http://meet.google.com/cto-jsdt-pay *Title*: Bayesian Calibration of Option Pricing Models Dr. Luca Gonzato, University of Vienna, Department of Statistics and Operations Research *Abstract*: Calibration of option pricing models to the implied volatility surface is a complicated, yet fundamental task in the quantitative finance community. By exploiting Sequential Monte Carlo (SMC) methods we turn the standard calibration problem into a Bayesian estimation task. In this way we can construct a sequence of distributions from the prior to the posterior which allows to compute any statistic of the estimated parameters, to overcome the strong dependence on the starting point and to avoid troublesome local minima; all of which are typical plagues of the standard calibration. To highlight the strength of our approach we consider the calibration of the double jump stochastic volatility model of Duffie et. al (2000) both on simulated and real option data. From the results on both single dates and time series of implied volatilities we find that our Bayesian approach largely outperforms the benchmark in terms of run time-accuracy, option pricing errors and statistical fit. Finally, we show how to further speed up computations by leveraging delayed-acceptance MCMC methods and deep learning. This is a joint work with R. Brignone, S. Knaust and E. Lutkebohmert, University of Friburg. ----------------------------------- Locandina dell'evento <https://drive.google.com/file/d/1cafNljjg5n3KSS9laizaryY8nkX6O6GD/view?usp=…> Seminari Matematici Statistici <https://sites.google.com/uniupo.it/seminari-ms/home-page> ----------------------------------- -- Enea Bongiorno, Università degli Studi del Piemonte Orientale Via Perrone 18, 28100, Novara, Italia Phone: +390321375317 enea.bongiorno(a)uniupo.it upobook.uniupo.it/enea.bongiorno

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Bando assegno di Ricerca MAT/06 a Napoli
by epirozzi 29 Jan '24

29 Jan '24

Si segnala il seguente bando per assegno di Ricerca presso il Dipartimento di Matematica e Applicazioni dell'Università di Napoli FEDERICO II con scadenza il 31 gennaio 2023 http://www.unina.it/documents/11958/48570136/DMA_AR-13_2023-12-11_bando.pdf Cari saluti Enrica -- Enrica Pirozzi Professore Associato di Probabilità e Statistica, Dipartimento di Matematica e Applicazioni, Università di Napoli FEDERICO II Via Cintia, 80126, NAPOLI https://www.docenti.unina.it/enrica.pirozzi

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Seminar in Statistics - LUCA TRAPIN
by Pierpaolo De Blasi 29 Jan '24

29 Jan '24

SEMINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO <https://www.carloalberto.org/events/category/seminars/seminars-in-statistic…> Venerdi 2 Febbraio 2024, alle ore 12.00, presso il Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terrà il seguente seminario: ------------------------------------------------ Speaker: Luca Trapin (Università di Bologna) Title: *Tail index regression forest* Abstract: Regression analysis of the tail index has received increasing attention over the last few years. The availability of large and complex datasets has stimulated the development of tail index regression techniques that can capture complex relationships between a large number of predictors and a dependent variable of interest. However, available methods are either flexible and asymptotically justified but do not scale well with the dimension of the predictor space, or well-suited in high-dimension but lack asymptotic results. This paper presents a novel random forest approach that fills this gap. We establish the asymptotic normality of our tail index regression forest estimator under mild assumptions on the tail behavior of the dependent variable. An extensive simulation study and an application to the conditional distribution of ROE for a large cross-section of U.S. companies confirm that our approach outperforms existing parametric and non-parametric tail index regression methods. ------------------------------------------------ Sarà possibile seguire il seminario anche in streaming: Join Zoom Meeting <https://us02web.zoom.us/j/82054178592?pwd=NHI1RHBLYnUwVUFoSW9HSVhUN3F6dz09> Il seminario è organizzato dalla "de Castro" Statistics Initiative www.carloalberto.org/stats -- Pierpaolo De Blasi University of Torino & Collegio Carlo Alberto carloalberto.org/pdeblasi <https://sites.google.com/a/carloalberto.org/pdeblasi/>

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