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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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Seminario di Scotti a Firenze, lunedì 23 aprile 14.30: Branching Processes with applications in Finance
by Cecilia Mancini 19 Apr '18

19 Apr '18

Il giorno lunedì 23 aprile, ore 14.30 Simone Scotti, Università Diderot di Parigi (Parigi 7), terrà il seminario dal titolo Branching Processes with applications in Finance presso l'edificio D6, via delle Pandette 9, 50127 Firenze, del polo di Novoli, in aula D6/104. Tutti gli interessati sono cordialmente invitati a partecipare Abstract: I processi di Branching con immigrazione (CBI) sono una classe di processi ben studiati in probabilità. In particolare i lavori di Dawson e Li hanno fornito una scrittura esplicita per l'EDS di un CBI generale e provato l'unicità della soluzione. Utilizzeremo questa scrittura per proporre un estensione del modello CIR includendo una parte di salto nel meccanismo di Branching, considerando in particolare il caso alpha-stabile per la sua parsimonia e la coerenza con i dati statistici. Questo modello ha mostrato di essere capace di riprodurre la persistenza dei bassi tassi d'interesse malgrado la presenza di importanti grandi salti. L'applicazione principale a cui ci interesseremo è un estensione del modello di Heston includendo salti auto-eccitati sul processo di varianza. Studiamo in particolare il comportamento della volatilità implicita dei derivati dell'azione e di quelli dei variance swap. Testiamo il modello sui dati del VIX. Basato su piu lavori con Guillaume Bernis, Ying Jiao, Chunhua Ma, Carlo Sgarra e Chao Zhou.

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seminario presso l'Università di Milano-Bicocca, 4 maggio 2018
by daniela bertacchi 18 Apr '18

18 Apr '18

Il giorno 4 maggio alle ore 14:30 in aula 3014 del Dipartimento di Matematica e Applicazioni dell'Università di Milano-Bicocca, la dottoressa Sophie Hautphenne dell'Università di Melbourne e Politecnico di Losanna, terrà un seminario dal titolo: The probabilities of extinction in a branching random walk on a strip Tutti gli interessati sono invitati a partecipare. Abstract. We consider a class of multitype Galton-Watson branching processes with countably infinite type set X whose mean progeny matrices have a block lower Hessenberg form. For these processes, we derive partial and global extinction criteria. Our approach involves embedding a finite-type explosive Galton-Watson process in a varying environment in the original infinite-type process, and then establishing asymptotic relationships between the two processes. We study the probability of extinction in sets of types A ⊆ X , q(A). In particular, we develop conditions for q(A) to be different from the global and partial extinction probability vectors. We present an iterative method to compute the vectors q(A), and investigate their location in the set of fixed points of the progeny generating vector.

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workshop Brescia 28, 29 September 2018
by Elena Vigna 17 Apr '18

17 Apr '18

Dear colleagues on 28 and 29 September 2018 a workshop will take place in Brescia on "LONGEVITY RISK MANAGEMENT, DIFFERENTIAL MORTALITY AND LIFETIME INEQUALITY" Theoretical and applied contributions on • longevity risk measurement and management • mortality risk measurement and management • affordability of pension schemes • risk management for pension funds • optimal pension time • differences in mortality rates: description, forecasting, risk management • best forecasting models for future actuarial measures • sources of lifetime inequalities: description, forecasting, risk management are welcome. More details attached. Regards, Elena Vigna -- Elena Vigna Università di Torino and Collegio Carlo Alberto https://sites.google.com/a/carloalberto.org/elena-vigna-web-page/home

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Ph. D. in Economics and Statistics, UNIMIB
by gianluca cassese 17 Apr '18

17 Apr '18

*Announcement* The Department of Economics, Management and Statistics (DEMS) of the University of Milano-Bicocca is happy to launch the new, four-year *PhD Program in Economics and Statistics* (ECOSTAT). Detailed information on program contents, available scholarships and application procedure for the academic year 2018-19 (cycle XXXIV) can be found here <http://www.unimib.it/unimib-international/phd/phd-programmes/ecostat>. ------------------------------------------------------------ --------------------------------------- -- *Gianluca Cassese* ------------------------------------------------------------------------------- Dipartimento di Economia, Metodi Quantitativi e Strategie di Impresa Università degli Studi di Milano-Bicocca via Bicocca degli Arcimboldi, 8 - 20126, Milan, Italy Building U7 - Room 2097 -------------------------------------------------------------------------------

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