webinars

Recent Advances in Functional Data Analysis

2021-04-29
The fourth “One World webinar” organized by YoungStatS will take place on June 30th, 2021. The topic of this webinar is on Functional Data Analysis. Selected young European researchers active in this area of research will present their contributions on spherical functional…
webinars

Composite-Based Structural Equation Modeling: Developments and Perspectives

2021-04-28
The third “One World webinar” organized by YoungStatS will take place on May 19th, 2021. The focus of this webinar will be on composite-based structural equation modeling, particularly on partial least squares path modeling (Wold, 1982; Lohmöller, 1989) and approaches to assess composite…
machine-learning

A small step to understand Generative Adversarial Networks

Gérard Biau, Benoît Cadre, Maxime Sangnier and Ugo Tanielian / 2021-04-27
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In the last decade, there have been spectacular advances on the practical side of machine learning. One of the most impressive may be the success of Generative Adversarial Networks (GANs) for image generation (Goodfellow et al. 2014). State of the art models are capable of producing portraits of…
hypothesis-testing

Generalizing the Neyman-Pearson Lemma for multiple hypothesis testing problems

Ruth Heller and Saharon Rosset / 2021-04-13
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Let us start by considering the optimal rejection policy for a single hypothesis testing problem. There are three elements to the problem. The objective: to maximize the power to reject the null hypothesis. The constraint: to control the type I error probability, so that it is at most a predefined…
webinars

Developments in Bayesian Nonparametrics (updated with slides)

2021-04-06
The second “One World webinar” organized by YoungStatS will take place on April 21st. The focus of this webinar will be on illustrating modern advances in Bayesian Nonparametrics data analysis, discussing challenging theoretical problems and stimulating case-studies within this active…
machine-learning

Analysis of a Two-Layer Neural Network via Displacement Convexity

Adel Javanmard, Marco Mondelli and Andrea Montanari / 2021-03-14
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We consider the problem of learning a function defined on a compact domain, using linear combinations of a large number of “bump-like” components (neurons). This idea lies at the core of a variety of methods from two-layer neural networks to kernel regression, to boosting. In general, the resulting…