February 2022 - Random - Mailing list — Dipartimento di Matematica, UniPI

Seminar José Figueroa Lopez in Verona, 23/2/22 at 12, Efficient Volatility Estimation for Lévy Processes with Jumps of Unbounded Variation
by Cecilia Mancini 16 Feb '22

16 Feb '22

José Figueroa Lopez Professor at the Washington University in St. Louis will give, both on site and on line, the SEMINAR Efficient Volatility Estimation for Lévy Processes with Jumps of Unbounded Variation Abstract. Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than a decade. One of the most well-known and widely studied problems is that of estimation of the quadratic variation of the continuous component of an Itô semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a Lévy process, we construct a new rate- and variance-efficient estimator for a class of Lévy processes of unbounded variation, whose small jumps behave like those of a stable Lévy process with Blumenthal-Getoor index less than 8/5. The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework. All interested people are warmly invited to participate Professor Figueroa-Lopez is visiting our department from February the 10th to March the 22nd, and from June the 15th to August the 15th For attendance via Zoom, please register at the following page, we will send you the meeting link: https://docs.google.com/forms/d/e/1FAIpQLSdJ6PccwEy8tSEngi0JMUKYEJ4yOHuuwur… Live attendance: the Department of Economics, via Cantarane 24, Vaona room. Due to the limited number of available seats, interested people should write an e-mail to: cecilia.mancini(a)univr.it<mailto:cecilia.mancini@univr.it>

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Scientific ASA 2022 conference on Data-Driven Decision Making
by Marta Nai Ruscone 15 Feb '22

15 Feb '22

Apologies for cross-posting.... Dear Colleagues, we would like to draw your attention to the upcoming Scientific ASA 2022 Conference on "Data-Driven Decision Making" that will be held at the University of Genoa (Italy), 12-14 September 2022. Attached is the Call for Paper. For further information and updates please visit the conference website asa2022.unige.it We are looking forward to Welcome you in Genova! Best Regards, Enrico di Bella and Corrado Lagazio (co-chairs of the Local Organising Commitee) *** --- *** <> Marta Nai Ruscone Department of Economics - DIEC University of Genova. Via Vivaldi, 5. 16100 Genova (GE). Italy

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International conference “Statistical Methods and Models for Complex - SAVE THE DATE!
by Alessandra Fabbri Colabich 11 Feb '22

11 Feb '22

*Si invia il seguente messaggio da parte della Direzione del Dipartimento di Scienze Statistiche dell'Università degli Studi di Padova:* *SAVE THE DATE!International conference “Statistical Methods and Models for Complex* Data”, *21-23 September 2022*, Padova, Italy The Department of Statistical Sciences of the University of Padova is organizing the international conference: “Statistical Methods and Models for Complex Data”. The conference will be held in Padova, Italy on 21-23 September 2022. The event is organized on the occasion of the 800th anniversary of the University of Padua and the last year of the excellence project of our department. The scientific program will include plenary sessions, with 45 minutes invited presentations from internationally recognized experts, followed by 25 minutes for interventions of two main discussants and informal moments of discussion and interaction between the participants. Partecipation of senior and young scientists involved in the developments of statistical methods and tools for complex data is strongly encouraged. The scientific program of the conference will be available as soon as possible on the website http://800years.stat.unipd.it, currently under development. For further information, please contact: 800years(a)stat.unipd.it

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Seminar in Statistics - GRAHAM CORMODE
by Pierpaolo De Blasi 11 Feb '22

11 Feb '22

SEMINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO <https://www.carloalberto.org/events/category/seminars/seminars-in-statistic…> Venerdi 18 Febbraio 2022, alle ore 12.00, presso il Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terrà il seguente seminario: ------------------------------------------------ Speaker: *Graham Cormode* (University of Warwick) Title: *New Lower and Upper Bounds for Quantile Summary Algorithms* Abstract: Finding the median, or more generally quantiles, is a core problem in data analysis. The question has been heavily studied in streaming and related models of computation, for over four decades. In this talk I will present some recent advances: - Lower bounds for approximating quantiles in the deterministic comparison model, for additive error, which show that the best known algorithm is in fact optimal - Upper bounds for relative error epsilon-approximations of quantiles, which improves over previous results and exceed the best known lower bounds by only an O(log(1/e)3/2) factor. This covers joint work with Pavel Vesely, Justin Thaler, Edo Liberty and Zohar Karnin. ------------------------------------------------ Sarà possibile seguire il seminario anche in streaming: Join Zoom Meeting <https://us02web.zoom.us/j/88206863118?pwd=cmlHYzBXbW9NZ2JVYmtDbDNqNnFydz09> Meeting ID: 882 0686 3118 Passcode: 757710 Il seminario è organizzato dalla "de Castro" Statistics Initiative www.carloalberto.org/stats Cordiali saluti, Pierpaolo De Blasi -- Pierpaolo De Blasi University of Torino & Collegio Carlo Alberto carloalberto.org/pdeblasi <https://sites.google.com/a/carloalberto.org/pdeblasi/>

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Third Italian Meeting on Probability and Mathematical Statistics
by Tiziano Vargiolu 10 Feb '22

10 Feb '22

Dear Colleagues, we are pleased to recall some essential information about the Third Italian Meeting on Probability and Mathematical Statistics (Bologna, Italy, June 13th-16th, 2022) Important deadlines: - March 30, 2022: submission of Organized Session proposals (https://site.unibo.it/probstat/en/how-to-contribute) - March 30, 2022: submission of abstracts of all contributions (https://site.unibo.it/probstat/en/how-to-contribute) - April 7, 2022: notification of acceptance will be sent to the authors - April 21, 2022: early registration (with reduced fee) - May 19, 2022 intermediate registration Main links: Conference web site: https://site.unibo.it/probstat/en Guidelines for authors: https://site.unibo.it/probstat/en/how-to-contribute Plenary Speakers: https://site.unibo.it/probstat/en/about-1/plenary Registration: https://site.unibo.it/probstat/en/registration Accommodation: https://site.unibo.it/probstat/en/for-participants/accommodation Travel info: https://site.unibo.it/probstat/en/for-participants/travel Contacts: https://site.unibo.it/probstat/en/contacts We look forward meeting you soon. The Scientific Committee Claudia Ceci, Paolo Dai Pra, Antonio Di Crescenzo, Franco Fagnola, Marco Ferrante, Franco Flandoli, Antonio Lijoi, Domenico Marinucci, Andrea Pascucci, Franco Pellerey, Laura Sacerdote The Local Organizing Committee Fabio Antonelli, Elena Bandini, Massimo Campanino, Andrea Cosso, Marco Di Francesco, Alberto Lanconelli, Stefano Pagliarani, Andrea Pascucci, Pietro Rigo, Tiziano Vargiolu

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(PMS)^2 online talk: Michele Coghi, Monday February 14 at 17.30
by Carlo Orrieri 09 Feb '22

09 Feb '22

Dear colleagues, We are happy to announce the following online talk: Speaker: Michele Coghi (Università d Trento) Title: Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering. Abstract: Motivated by the challenge of incorporating data into misspecified and multiscale dynamical models, we study a McKean-Vlasov equation that contains the data stream as a common driving rough path. This setting allows us to prove well-posedness as well as continuity with respect to the driver in an appropriate rough-path topology. The latter property is key in our subsequent development of a robust data assimilation methodology: We establish propagation of chaos for the associated interacting particle system, which in turn is suggestive of a numerical scheme that can be viewed as an extension of the ensemble Kalman filter to a rough-path framework. Finally, we discuss a data-driven method based on subsampling to construct suitable rough path lifts and demonstrate the robustness of our scheme in a number of numerical experiments related to parameter estimation problems in multiscale contexts. Based on arXiv:2107.06621 <https://arxiv.org/abs/2107.06621>. Date and time: Monday February 14, 17:30-18:30 (Rome time zone) Zoom link: https://us02web.zoom.us/j/88138955113?pwd=R1Vpa2xWVDlFc2pjUXIxSWpWU21LQT09 <https://us02web.zoom.us/j/88138955113?pwd=R1Vpa2xWVDlFc2pjUXIxSWpWU21LQT09> ID riunione: 881 3895 5113 Passcode: 672537 This talk is the second of the (PMS)^2: Pavia-Milano Seminar series on Probability and Mathematical Statistics organized jointly by the universities Milano-Bicocca, Pavia, Milano-Politecnico and Milano-Statale. Participation is free and welcome! (though limited to 100 participants for technical reasons) Best regards The organizers (Mario Maurelli, Carlo Orrieri, Maurizia Rossi, Margherita Zanella)

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Online workshop announcement
by Alessandra Micheletti 08 Feb '22

08 Feb '22

*Big Data challenges for Mathematics: state of the art and future perspectives* *Online webinar – February 25, 2022* The workshop will be held on the platform Zoom and will be streamed on YouTube. Participants who would like to follow the workshop in real time and participate to the discussion with the speakers are requested to register. See the web site of the workshop for registration, programme and additional information: *http://itn-bigmath.unimi.it/* <http://itn-bigmath.unimi.it/> *Aims of the workshop:* Nowadays the data deluge is the hallmark of a new kind of ‘Law of Large Numbers’ that builds intelligence from large, heterogeneous, noisy and, in general, complex data sets collected from mobile devices, the Internet of Things, software logs, automated medical devices, social media, and so many other data sources. The classical mathematical and statistical paradigms are often not applicable to current real-world problems, so there is a growing demand of new mathematical and statistical techniques to face such problems, able to shed some light on the often black-box techniques which are usually applied in such context and which characterise Deep Learning and, more generally, Artificial Intelligence. On the other side, since computers can not do everything by themselves, there is a growing need for new professional and scientific figures, the data scientists, that master a whole range of skills, ranging from data processing to sophisticated math tools and computational skills that are needed to extract the knowledge. The state of the art and future research perspectives in this framework will be highlighted and discussed in this webinar by the PhD students enrolled in the EU funded MSCA Project BIGMATH <http://itn-bigmath.unimi.it/> (grant n. 812912), starting from a set of challenging industrial case studies. Additionally some well known keynote speakers will introduce their point of view on possible future perspectives in different specific and quite hot subject areas related with the analysis of complex and big data. -- ------------------------------------ Alessandra Micheletti Associate Professor - Probability and Mathematical Statistics Dept. of Environmental Science and Policy - ESP Università degli Studi di Milano via Saldini 50, 20133 Milano, Italy phone: +39-02503-16130 fax: +39-02503-16090 https://alessandramichelettiwebpage.wordpress.com/

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Seminar by Sara Svaluto-Ferro in Padova.
by Giorgia Callegaro 07 Feb '22

07 Feb '22

Dear all, it's a pleasure to announce the next Probability and Finance seminar at the Department of Mathematics of the University of Padova, which will be held both *in presence* and online with the following details: - Date: 25 February 2022 - Speaker: Dr. Sara Svaluto-Ferro (Univ. of Verona) - Venue: Dept of Mathematics, Univ. of Padova, room 2BC30 - Zoom link: available at https://www.math.unipd.it/~bianchi/seminari/ - Title: "*Signature processes in mathematical finance, an introduction*" - Abstract: we will provide an introduction to the signature of a continuous semimartingale. This in particular includes its definition, its main properties, and an overview of (some) of its uses in financial mathematics. Thanks and hope to see you soon, Giorgia -- Giorgia Callegaro Associate Professor Department of Mathematics - University of Padova Via Trieste 63 , I-35121 Padova - ITALY Tel: +39-0498271481 Fax: +39-0498271499 E-Mail: gcallega(a)math.unipd.it <https://webmail.math.unipd.it/horde3/imp/message.php?mailbox=Sent&index=598#> Personal web-page: https://sites.google.com/site/giogiocallegaro/Home

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ERC funded postdoctoral position at University of Torino and Collegio Carlo Alberto
by Stefano Favaro 03 Feb '22

03 Feb '22

The Department ESOMAS at University of Torino and Collegio Carlo Alberto invites applications for a postdoctoral position within the European Research Council (ERC) project “Nonparametric Bayes and empirical Bayes for species sampling problems: classical questions, new directions are related issues”. The general area of interest is Statistics. Relevant details of the postdoctoral position ara available at the bottom of this letter. Deadline for applications: 22 FEBRUARY 2022 Information for applicants are available (only in Italian) at the Call for Applications https://pica.cineca.it/unito/assegni-di-ricerca-unito-2022-i/file/Bando%20d… <https://pica.cineca.it/unito/assegni-di-ricerca-unito-2022-i/file/Bando%20d…>. Within the Call for Applications, the postdoctoral position appears at page 24 under the title “Statistica Bayesiana non-parametrica e non-parametrica empirica per problemi di campionamento di specie”. Reference code: ESOMAS.2022.01. Applications are made only online - https://pica.cineca.it/unito/ <https://pica.cineca.it/unito/> - by selecting “Your Applications” in the box “Bando Assegni di ricerca - Tornata I 2022” (code TornataI2022). The application procedure is available in Italian/English, and it requires a CV, two reference letters and a research statement. Prospective candidates may contact directly Stefano Favaro - stefano.favaro(a)unito.it <mailto:stefano.favaro@unito.it> - for any information on the postdoctoral position and the application procedure. Best wishes Stefano Favaro **** Prospective candidates are expected to have experience on nonparametric statistics, within the classical (frequentist) and/or Bayesian paradigm, and they should preferably be holding a Ph.D. or being close to receiving one. The research shall be carried out in English. The duration of the contract is 24 months. Expected starting date in May 2022, but a different date may be arranged. The salary amounts to 46,000 Euros per year, including taxes and social charges, and considerable financial support to attend conferences and workshops will be granted. There are no teaching duties associated to the position. Abstract. Object of research are species sampling problems, whose importance has grown considerably in recent years driven by numerous applications in the broad area of biosciences, and also in machine learning, theoretical computer science and information theory. Within the broad field of species sampling problems, the research will be focussed on two research themes: i) the study of nonparametric Bayes and nonparametric empirical Bayes methodologies for classical species sampling problems, generalized species sampling problems emerging in biological and physical sciences, and question thereof in the context of optimal design of species inventories; ii) the use of recent mathematical tools from the theory of differential privacy to study the fundamental tradeoff between privacy protection of information, which requires to release partial data, and Bayesian learning in species sampling problems, which requires accurate data to make inference. **** -- Stefano Favaro University of Torino and Collegio Carlo Alberto http://sites.carloalberto.org/favaro/ <http://sites.carloalberto.org/favaro/>

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Prossimi seminari PRISMA
by Domenico Marinucci 01 Feb '22

01 Feb '22

Cari colleghi, scusandomi per eventuali ripetizioni, vi annuncio i prossimi due seminari della serie UMI-Prisma, che si svolgeranno online su piattaforma teams lunedì prossimo 7 febbraio dalle 16 alle 18: ------------------------------------------------------------------------------------------------------------------- Speaker: Ernesto De Vito, Dipartimento di Matematica e MaLGa Center, Università di Genova Title: Empirical risk minimization: old and new results Abstract: The first part of the talk is devoted to a brief introduction to supervised learning focusing on the regularised empirical risk minimization (ERM) on Reproducing Kernel Hilbert spaces. Though ERM achieves optimal convergence rates [1], it requires huge computational resources on high dimensional datasets. The second half of the talk is devoted to discuss some recent ideas where the hypothesis space is a low dimensional random space. This approach naturally leads to computational savings, but the question is whether the corresponding learning accuracy is degraded. If the random subspace is spanned by a random subset of the data, the statistical-computational tradeoff has been first explored for the least squares loss [2,3], for the least squares loss, then for self-concordant loss functions [4] , as the logistic loss, and, quite recently, for non-smooth convex Lipschitz loss functions [5], as the hinge loss. References: [1] Caponnetto, A. and De Vito, E. (2007). Optimal rates for the regularized least-squares algorithm. Foundations of Computational Mathematics, 7(3):331–368. [2] Rudi, A., Calandriello, D., Carratino, L., and Rosasco, L. (2018). On fast leverage score sampling and optimal learning. In Advances in Neural Information Processing Systems, pages 5672–5682. [3] Rudi, A., Camoriano, R., and Rosasco, L. (2015). Less is more: Nystrom computational regularization. In Advances in Neural Information Processing Systems, pages 1657–1665. [4] Marteau-Ferey, U., Ostrovskii, D., Bach, F., and Rudi, A. (2019). Beyond least-squares: Fast rates for regularized empirical risk minimization through self-concordance. arXiv preprint arXiv:1902.03046. [5] Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco, Regularized ERM on random subspaces arXiv:2006.10016 Speaker: Alessandro Rudi, ENS Paris Title: "Representing non-negative function with applications to non-convex optimization and beyond" Abstract: In this talk we present a rather flexible and expressive model for non-negative functions. We will show direct applications in probability representation and non-convex optimization. In particular, the model allows to derive an algorithm for non-convex optimization that is adaptive to the degree of differentiability of the objective function and achieves optimal rates of convergence. Finally, we show how to apply the same technique to other interesting problems in applied mathematics that can be easily expressed in terms of inequalities. References: Ulysse Marteau-Ferey , Francis Bach, Alessandro Rudi. Non-parametric Models for Non-negative Functions. https://arxiv.org/abs/2007.03926 Alessandro Rudi, Ulysse Marteau-Ferey, Francis Bach. Finding Global Minima via Kernel Approximations. https://arxiv.org/abs/2012.11978 ------------------------------------------------------------------------------------------------------------------------------ Il link teams per partecipare è il seguente: https://teams.microsoft.com/l/meetup-join/19%3a667d2414be564c5d8fba30acffeb… Grazie e saluti, Domenico Marinucci -- @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Domenico Marinucci Dipartimento di Matematica Università di Roma Tor Vergata https://www.mat.uniroma2.it/~marinucc/ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

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