April 2018 - Random - Mailing list — Dipartimento di Matematica, UniPI

(senza oggetto)
by Faggionato, Alessandra 24 Apr '18

24 Apr '18

Buongiorno, e' stato pubblicato sul sito web del Dipartimento il seguente bando per assegno di ricerca: https://www2.mat.uniroma1.it/sites/default/files/bando2018-2.pdf n. 2/2018 per il conferimento di n. 2 assegni annuali categoria A tip. II _ Matematica e sue applicazioni. SSD: tutti scadenza: 23 maggio 2018 saluti af -- ************************************************* Prof. Alessandra Faggionato http://www1.mat.uniroma1.it/~faggionato/ Department of Mathematics University "La Sapienza" Piazzale Aldo Moro, 5 00185 - Rome Office 5, Phone (0039) 06 49913252 *************************************************

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International Conference in Venice - CALL FOR PAPERS
by Monica Billio 22 Apr '18

22 Apr '18

CREDIT2018 <http://www.greta.it/credit/credit2018/credit2018.htm> *GRETA Associati *(Venice, Italy), the *European Investment Fund *(EIF, Luxembourg), *European DataWarehouse*(Frankfurt am Main, Germany) and *Intesa Sanpaolo *(Milan, Italy) are co-sponsors of a Conference to be held in Venice on September 27-28, 2018. The objective of the Conference is to bring together academics, practitioners and PhD students working in the area of risk management. The conference will provide an opportunity for participants engaged in research at the forefront of this area to discuss both the causes and implications of recent events in financial markets and may, in turn, suggest fruitful directions for future research. The Conference, organised under the auspices of the *Department of Economics of the University Ca’ Foscari of Venice*, *ABI - Italian Banking Association *and *European Investment Bank*, is the *seventeenth* of a series dedicated to various aspects of credit risk. //Since 2007, the financial crisis has triggered deep changes of various kinds. Central banks have experimented with new monetary policies (low interest rates, quantitative easing, forward guidance...). In the meantime the first steps towards a more neutral monetary policy are taken. The banking industry has had to develop new asset management strategies and to adjust its credit allocation practices in order to cope with a new environment characterized, in particular, by a prolonged period of very low risk-free returns. Meanwhile, regulatory frameworks have been reshaped all around the world, with the introduction of new requirements or guidelines including regulation on credit impairment provisioning. All t////hese changes have called for a substantial amount of research to improve the knowledge of the mechanisms at play during the crisis, to design adapted policy tools and to provide a better description of the new economic and financial environment.////// The organizers encourage submissions of papers on any topic within the overall theme of the conference and in the following areas in particular: * Loan level data and credit risk analysis: credit registries; big data analytics; credit risk measurement; credit risk modelling; * Financial regulation and credit cycles: macro-prudential regulation; banking cycles; bank business models; rational inattention; * Macro-economic determinants of SMEs’ credit risk and SME loan portfolios: ECB policy; macro-parameters for SMEs’ credit risk; industry sector specialization; loan portfolio allocation; * IFRS9, credit impairments and SME financing: expected credit loss; internal models versus standardized approach; procyclicality; * The economic impact of government support measures (i.e. Credit Guarantee Schemes, Venture Capital): availability; real effects; inequality; * Monetary policy, financial regulation and credit cycles: unconventional monetary policy; monetary policy normalization; macro-prudential tools; banking cycles; ·Macro-prudential regulation and real estate valuation: macro-prudential tools; loan-to-value; loan-to-income; collateral value; real estate prices. The final program will include both submitted and invited papers. Acceptances received so far from invited speakers include *Tor Jacobson *(Sveriges Riksbank), *Robert C. Merton* (MIT Sloan School of Management), and *Guillaume Plantin *(Sciences Po & CEPR). The Conference will also feature a panel discussion on researchers' and practitioners' views of the major outstanding problems. Sponsors** *GRETA* <http://www.greta.it/> *EIF* <http://www.eif.org/index.htm> *European Datawarehouse* <https://eurodw.eu/> *IntesaSanpaolo* <http://www.group.intesasanpaolo.com/scriptIsir0/si09/eng_index.jsp> Auspices** *ABI* <http://www.abi.it/Pagine/default.aspx> *EIB* <http://www.eib.org/> *Ca' Foscari - Economics Department* <http://www.unive.it/pag/16892> The Scientific Committee consists of: *Hans Degryse *(KU Leuven, Halle Institute for Economic Research & CEPR, Programme Chair)** *Monica Billio *(Ca’ Foscari University of Venice & GRETA) *Vasso Ioannidou *(Lancaster University & CEPR) *Helmut Kraemer-Eis *(Head of Research & Market Analysis, Chief Economist, EIF) *Jan Pieter Krahnen *(SAFE - Goethe University) *Steven Ongena *(University of Zurich, Swiss Finance Institute, KU Leuven & CEPR) *Guillaume Plantin*(Sciences Po & CEPR) *Stephen Schaefer *(London Business School) *Enrico Sette *(Bank of Italy) *Ludovic Thebault *(European DataWarehouse). *CALL FOR PAPERS* Those wishing to present a paper at the Conference should submit by *May 25, 2018 *to the address given below (preferably in electronic format). Please indicate to whom correspondence should be addressed. Decisions regarding acceptance will be made by *June 25, 2018*. The final version of accepted papers must be received by August 31, 2018. European DataWarehouse (Frankfurt am Main, Germany) will offer access to its ABS loan level database <https://eurodw.eu/> to the authors of the selected papers on the following topics: loan level data, credit risk analysis, loan level data analytics, credit risk measurement, credit risk modelling, loan-to-value, loan-to-income, collateral value, real estate valuations. The access to the database will be given for a period of 1 year. Please send papers to: GRETA Associati San Polo, 2605 - 30125 Venice, ITALY Phone : +39 041 5238178 e-mail: credit(a)greta.it <mailto:credit@greta.it> *REGISTRATION* To register for the Conference you are requested to complete the registration form that is available on our website (_http://www.greta.it/credit/credit2018/credit2018.htm_). Registration fees are: Late registration (from September 1^st on) Early registration (within August 31^st ) Academics: 250 Euro + VAT 300 Euro + VAT Practitioners: 800 Euro + VAT 1000 Euro + VAT Academics Poster Presenter: 130 Euro + VAT 130 Euro + VAT Practitioners Poster Presenter: 500 Euro + VAT 500 Euro + VAT PhD Students*: 80 Euro + VAT 130 Euro + VAT ** *VAT is currently 22%* For participants presenting a paper there are no fees. *Students will have to provide valid proof of their student status. The registration fees cover: * Admission to all scientific sessions * Lunches and coffee service during the Conference * Conference kit The registration fees do not fully cover the conference dinner on September 27th, 2018, for which there is an extra charge of 90.00 Euro per person (conference attendees as well as accompanying persons). *IMPORTANT DATES* May 25, 2018: Papers submission deadline June 25, 2018: Paper acceptance notification August 31, 2018: Deadline for sending final version of accepted papers August 31, 2018: Deadline for early registration More detailed information available on the Conference website: http://www.greta.it/credit/credit2018/credit2018.htm *** Please accept our apologies for any crossed e-mails. ** If you do not want to receive more information from this e-mailing list, please reply to this message with Subject "Cancel from Mailing List". -- Nota automatica aggiunta dal sistema di posta.

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DGA-Inria PhD position at Sophia-Anitipolis, France. Deadline : April 29th 2018. Contact : Josiane ZERUBIA, Inria-SAM
by Marianne Clausel 21 Apr '18

21 Apr '18

*Application :* For more information and application, please contact before April 29 th , 2018: Josiane ZERUBIA, Inria-SAM http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html email: Josiane.Zerubia(a)inria.fr *Title:* Stochastic Geometry for Multiple Object Detection and Tracking in High Resolution Multi-Source Data Sets for Wide Area Surveillance Applications *Abstract:* Unmanned aerial vehicles and low-orbit satellites, including cubesats, are increasingly used for wide area surveillance which results in large amounts of multi- source data (videos) that have to be processed and analyzed. These sensor platforms capture vast ground areas at roughly 2 frames per second. The number of moving objects in such data is typically very high, accounting for up to thousands of objects. Multiple objects tracking has traditionally been a major area of research in the computer vision field, but this type of data poses new, specific, tracking related challenges. The large number of small objects coupled with the reduced frame rate of the video, illumination changes and image registration provide significant sources of errors. Numerous motion models and state estimation methods like the Kalman filter or the particle filter have been proposed for object tracking. Classical trackers such as the Multiple Hypothesis Tracker or the Joint Probabilistic Data Association Filter have been employed to solve the data association problem between multiple detections. Both approaches work on a set of data association hypothesis. A strong limitation of these methods is that past decisions cannot be updated when new information is available. One way to cope with this problem is to use a sliding temporal window to perform tracking taking into account both past and future information and hence, removing the causality of the result. Recently, a new spatio-temporal marked point process model specifically adapted to the problem of multiple objects tracking has been developed by Craciun et al. [1]. Craciun et al. use ellipses to model the objects, boats or cars for instance, adding a non-geometric mark to facilitate the association between objects in different frames. Nevertheless one important drawback of the above-mentioned model is that constant velocity of the moving objects is a necessary prior hypothesis to deal with the corresponding density function to be optimized. In this PhD thesis we propose to get rid of this constraint by extending the previous model of Paula Craciun . [1] P. Craciun, M. Ortner, and J. Zerubia. Joint detection and tracking of moving objects using spatio-temporal marked point processes. Proc. IEEE Winter Conference on Applications of Computer Vision, 2015. *Candidate profile:* We encourage applications from outstanding candidates with strong academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields. At Inria we seek to increase the number of women in areas where they are underrepresented and therefore explicitly encourage women to apply. Furthermore, we are committed to increasing the number of individuals with disabilities in its workforce and therefore encourage applications from such qualified individuals. *Important notice: to get PhD funding from DGA it is necessary to be a** **European citizen (EU and associated countries).*

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Kondratiev minicourse in Verona: cancelled
by Luca di persio 21 Apr '18

21 Apr '18

Dear all, I am regret to announce that the forecasted course of prof. Kondratiev http://www.di.univr.it/?ent=seminario&id=4253&lang=en has been cancelled due to serious health conditions of the speaker. Do not hesitate to contact me if you need further info. Best, LuCa -- __ Luca Di Persio - PhD College of Mathematics Dept. of Computer Science - University of Verona strada le Grazie 15 - 37134 Verona - Italy Tel : +39 045 802 7968 <http://www.hpa8.com/>

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PhD Thesis : Towards hybrid and explainable recommender systems mixing content analysis and collaborative filterings, Project OLKI Lorraine Université d'Excellence, France
by Marianne Clausel 20 Apr '18

20 Apr '18

*General context* Over the last twenty years, an increasing attention has been paid to recommender systems, widely popularized by the Netflix Challenge. The main goal of a recommender system is to provide some users, with personalized products, taking into account their profile and preferences. Recent challenges are about the recommendation of products very complex to describe : jobs, partners... Their characteristics can mix heterogeneous features: quantitative (as ratings) and/or qualitative (as reviews). Moreover, new questions are emerging about explainability of algorithms. Nowadays, Artificial Intelligence algorithms are democratized in our erveyday life, and consumers want to understand the decision resulting from these algorithms (why this decision and not another one ?) as well as quantify the importance of each factor (element) in the decision process (which element is the most important/sensitive). They require more /explainability/ of AI algorithms. In addition, the new European legislation on data protection foresees to impose more /transparency/ to Artificial Intelligence algorithm. The law envisages to make compulsory the agreement of users for using personal data, which will reduce the amount of data that can be collected about users. The customer will also have to be informed about the way their personal data is used. From the algorithms point of view, the decrease of data will impact the quality of the recommmendations. All these changes, will impact shortly and significantly the design of algorithms. In this thesis, we aim at designing and implementing new explainable and transparent recommender systems for complex products, in the frame of data sparsity. *Scientific challenges and program* The challenges are four fold : - *Definition*, in a quantitative way, of the concept of transparency, and develop statistical methods to automatically quantify the transparency degree of an algorithm. - *Classification* of recommender systems from the literature, from the transparency point of view and/or robustness degree with respect to missing data - *Conception* of new hybrid and explainable recommender systems, robust to sparse data. The products being complex, the heterogeneous descriptions of the products, as well as the multi-sources of information, will be used to construct understandable explanation. Especially, natural language processing, and hybrid (content/social) approaches will be studied. The algorithms will also be able to quantify the weights and the sensitivity of each factor in the final decision. - *Constitution *of data sets, allowing to evaluate transparency of recommender systems *Application* \noindent The application should include a brief description of research interests and past experience, a CV, degrees and grades, a copy of Master thesis (or a draft thereof), motivation letter (short but pertinent to this call), relevant publications (if any), and other relevant documents. Candidates are encouraged to provide letter(s) of recommendation or contact information to reference persons. Please send your application *before 12 May 2018* in one single pdf to : armelle.brun(a)univ-lorraine.fr marianne.clausel(a)univ-lorraine.fr The application of the preselected candidates will be reviewed by the Doctoral School IAEM of University of Lorraine in June 2018 for completing the selection process. *Practical informations* *Duration: *3 years (full time position) *Starting date:* October, 2018 *Supervisors* A. Brun, University of Lorraine/LORIA, France, https://members.loria.fr/ABrun/ M. Clausel, University of Lorraine/IECL, France, https://sites.google.com/site/marianneclausel/ *Working Environment* The PhD candidate will work between the Probability and Statistic team of the IECL lab and the KIWI Team of the LORIA lab which are two leading institutions, respectively in Mathematics and Computer Science in France. The two labs are both located at Nancy, France on the same campus. The Probability and Statistic team of IECL is working on interdisciplinary projects involving probabilistic modeling and inference methods, with a focus on many applications as textual datas, biology, spatial datas... The KIWI team of LORIA is a dynamic group working on recommender system and connected scientific domains over 20 researchers (including PhD students) and that covers several aspects of the subject from theory to applications, including statistical learning, data-mining, and cognitive science. *Location* : Nancy, which is the capital of Lorraine in France, with excellent train connection to Luxembourg (1h30) and Paris (1h30). *Salary after taxes: *around 1600 euros.

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Fwd: 4th Barcelona Summer School on Stochastic Analysis
by Marco Romito 20 Apr '18

20 Apr '18

-------- Forwarded Message -------- Subject: 4th Barcelona Summer School on Stochastic Analysis Date: Fri, 20 Apr 2018 12:11:45 +0200 From: Bústia Difusions <crmdifusions(a)crm.cat> To: romito(a)dm.unipi.it We would like to bring your attention to the forthcoming *4th Barcelona Summer School on Stochastic Analysis* *A 2018 EMS Summer School* which will be held at the Centre de Recerca Matemàtica <http://www.crm.cat/en/About/General/Pages/LocationDirections.aspx>in Bellaterra (Barcelona) from *July 9^th to July 13^th , 2018*. The school is addressed to PhD students and young researchers mainly in stochastic analysis. The courses will be the following: * /Some stochastic models in eco-evolution/, by *Sylvie Méléard*(École Polytechnique, France) * /Zero sets of random functions, by**/*Mikhail Sodin*(Tel Aviv University, Israel) -Registration deadline: *June 1^st , 2018. * The period for submissions of proposals for poster presentations and contributed talks is currently open. The deadline is *May 1^st , 2018*.** We invite you to visit the homepage of the school www.crm.cat/2018/StochasticAnalysisBCN <http://www.crm.cat/2018/StochasticAnalysisBCN>to keep posted with the progress of and news about the organization. *Organizing Committee* Xavier Bardina (Universitat Autònoma de Barcelona) Lluís Quer-Sardanyons (Universitat Autònoma de Barcelona) Marta Sanz-Solé (Universitat de Barcelona) Josep Vives (Universitat de Barcelona) __________________ *Centre de Recerca Matemàtica* Campus de Bellaterra, Edifici C 08193 Bellaterra (Barcelona) Tel.: + 34 93 581 10 81 Fax: + 34 93 581 22 02 *www.crm.cat* <http://www.crm.cat/>** *@CRMatematica* <https://twitter.com/CRMatematica>**

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Postdoc position in Geometric Methods for Deep and Reinforcement Learning at RIST
by Luigi Malagò 20 Apr '18

20 Apr '18

=========================================================== *Subject*: Postdoc position in Machine Learning (1 year, renewable up to 2.5 years) *Institution*: RIST - Romanian Institute of Science and Technology, Cluj-Napoca *Keywords*: Deep Learning, Reinforcement Learning, Stochastic Optimization, Optimization over Manifolds, Information Geometry, Riemannian Geometry *Application deadline*: 06 May 2018 (applicants are encouraged to apply earlier) *Salary*: around 2190 euro net *Official announcement*: http://rist.ro/en/details/news/postdoc-positio ns-in-deep-learning-and-machine-learning.html =========================================================== Dear colleagues, the Romanian Institute of Science and Technology (RIST) has an opening for a postdoc position, in the context of the DeepRiemann project “Riemannian Optimization Methods for Deep Learning”, funded by European structural funds through the Competitiveness Operational Program (POC 2014-2020). The appointments will be for 1 year, with possible extensions up to 2.5 years. The DeepRiemann project aims at the design and analysis of novel training algorithms for Neural Networks in Deep Learning, by applying notions of Riemannian optimization and differential geometry. The task of the training a Neural Network is studied by employing tools from Optimization over Manifolds and Information Geometry, by casting the learning process to an optimization problem defined over a statistical manifold, i.e., a set of probability distributions. The project is highly interdisciplinary, with competences spanning from Machine Learning to Optimization, Deep Learning, Statistics, and Differential Geometry. The objectives of the project are multiple and include both theoretical and applied research, together with industrial activities oriented to transfer knowledge, from the institute to a startup or spin-off of the research group. The positions will be part of the new Machine Learning and Optimization group http://luigimalago.it/group.html, which performs research at the intersection of Machine Learning, Stochastic Optimization, Deep Learning, and Optimization over Manifolds, from the unifying perspective of Information Geometry. The group is one of two newly-formed groups in Machine Learning at RIST, where about 20 new postdoctoral research associates and research software developers will be hired by 2018. The official job announcement can be seen here: http://rist.ro/en/details/news/postdoc-positions-in-deep- learning-and-machine-learning.html Informal inquiries can be sent to Dr. Luigi Malagò <malago(a)rist.ro>, principal investigator of the DeepRiemann project. Application deadline: 06 May 2018 (applicants are encouraged to apply earlier) best regards, Luigi Malagò

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Seminari SOYER e POLSON al CNR IMATI - Milano - 3 Maggio
by fabrizio＠mi.imati.cnr.it 20 Apr '18

20 Apr '18

Segnalo i seguenti seminari che si svolgeranno presso CNR IMATI, via Alfonso Corti 12, Milano, Aula Expo Giovedì 3 maggio 2018, ore 15:00 - 16:30 1) Refik Soyer, School of Business, George Washington University ore 15:00-15:45 BAYESIAN MODELING OF NON GAUSSIAN MULTIVARIATE TIME SERIES Abstract: Modeling of multivariate non Gaussian time series of correlated observations is considered. In so doing, we focus on time series from multivariate counts and durations. Dependence among series arises as a result of sharing a common dynamic environment. We discuss characteristics of the resulting multivariate time series models and develop Bayesian inference for them using particle filtering and Markov chain Monte Carlo methods. We illustrate application of the proposed approach using conditionally multivariate Poisson and gamma time series. Joint work with Tevfik Aktekin, University of New Hampshire and Nicholas Polson, University of Chicago 2) Nicholas Polson, Booth School of Business, University of Chicago ore 15:45-16:30 DEEP LEARNING: A BAYESIAN PERSPECTIVE Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, such as principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are all shown to be shallow learners. Their deep learning counterparts exploit multiple deep layers of data reduction which provide predictive performance gains. Stochastic gradient descent (SGD) training optimisation and Dropout (DO) regularization provide estimation and variable selection. Bayesian regularization is central to finding weights and connections in networks to optimize the predictive bias-variance trade-off. To illustrate our methodology, we provide an analysis of international bookings on Airbnb. Finally, we conclude with directions for future research. Joint work with Vladimir Sokolov, Sistems Engineering and Operations Research, George Mason University -- Fabrizio Ruggeri fabrizio AT mi.imati.cnr.it CNR IMATI tel +39 0223699532 Via Bassini 15 fax +39 0223699538 I-20133 Milano (Italy) web.mi.imati.cnr.it/fabrizio

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Seminario KYPRAIOS
by Matteo Ruggiero 20 Apr '18

20 Apr '18

STATISTICS SEMINARS @ COLLEGIO CARLO ALBERTO Venerdì 27 Aprile 2018, alle ore 12:00, presso il nuovo edificio del Collegio Carlo Alberto, in Piazza Arbarello 8, Torino, si terrà il seguente seminario: ------------------------------------------------ Theodore KYPRAIOS (University of Nottingham) LATENT BRANCHING TREES: MODELLING AND BAYESIAN COMPUTATION In this talk a novel class of semi-parametric time series models will be presented, for which we can specify in advance the marginal distribution of the observations and then build the dependence structure of the observations around them by introducing an underlying stochastic process termed as 'latent branching tree'. It will be demonstrated how can we draw Bayesian inference for the model parameters using Markov Chain Monte Carlo methods as well as Approximate Bayesian Computation methodology. Finally a real dataset on genome scheme data will be fitted to this model and we will also discuss how this kind of models can be in other settings. ------------------------------------------------ Tutti gli interessati sono invitati a partecipare. Il seminario è organizzato dalla "de Castro" Statistics Initiative (www.carloalberto.org/stats <http://www.carloalberto.org/stats>) in collaborazione con il Collegio Carlo Alberto. Cordiali saluti, Matteo Ruggiero --- Matteo Ruggiero University of Torino and Collegio Carlo Alberto www.matteoruggiero.it

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First announcement - workshop on BSDEs, Information and McKean-Vlasov equations, 10-12 Sept, Leeds
by Elena Issoglio 19 Apr '18

19 Apr '18

Dear colleagues, We are pleased to announce a Workshop on “BSDEs, Information and McKean-Vlasov equations”, which will take place on September 10th - 12th, 2018 at the University of Leeds, UK. The objective of the meeting is to disseminate recent results on these mainstream topics in stochastic analysis and bring together experts from many parts of Europe to promote the interplay of ideas and techniques. Additional information can be found on the website of the event https://conferences.leeds.ac.uk/bsdes2018/. Confirmed plenary speakers for the workshop are: Claudia Ceci, University of Pescara Dan Crisan, Imperial College London Samuel Cohen, University of Oxford Miryana Grigorova, University of Bielefeld Said Hamadene, University of Main Ben Hambly, University of Oxford Gechun Liang, University of Warwick  Huyen Pham, University of Paris Diderot Marie-Claire Quenez, University of Paris Diderot Francesco Russo, Ensta ParisTech Lukasz Szpruch, University of Edinburgh Participants can register via the following link https://conferences.leeds.ac.uk/bsdes2018/register/ Registration closes on July 16th, 2018. A registration fee of £70 will cover lunch and coffee breaks for the three days, and a buffet dinner on the first evening of the conference. There is a limited budget to cover expenses for young participants. All details are specified on the website. Submission of contributed talks is currently open. There will be also a poster session to allow young researchers working on these topics to present their ideas and results. If you wish to submit an abstract for a contributed talk or a poster please email Katia at k.colaneri(a)leeds.ac.uk or Elena at e.issoglio(a)leeds.ac.uk before June 25th, 2018. Notification of acceptance will be given by July 2nd, 2018. We hope very much that you will consider to participate in these events, and please feel free to forward this email to any interested people. We are looking forward to meeting you in Leeds. Best regards, The organizing committee Elena Issoglio and Katia Colaneri Dr Elena Issoglio Lecturer in Financial Mathematics Office 11.02, School of Mathematics School of Mathematics, University of Leeds, Leeds, LS2 9JT E: e.issoglio(a)leeds.ac.uk<mailto:e.issoglio@leeds.ac.uk> T: 0113 34 3 4660

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