Conference on Structural Inference in Statistics

[Odalric-Ambrym Maillard]

Conference on Structural Inference in Statistics

September 17 to 19, 2013
Potsdam, Germany

The aim of the conference is to bring together and foster exchanges between experts of mathematical statistics and of different neighboring fields relevant to structural inference. The combination of theoretical, methodological and applied aspects also aims at identifying possible new seminal directions of research.

The conference will feature two keynote mini-lecture series by

• Sanjoy Dasgupta (University of California, San Diego) on combining algorithmic theory with geometry and statistics, and
• Stéphane Mallat (École Polytechnique, Paris) on approaches linked to signal processing and approximation theory.

Confirmed invited speakers are (the list will be updated as we get confirmation from additional invited speakers)

• J. Jin (Carnegie Mellon University),
• A. Juditsky (Université Joseph Fourier, Grenoble) (tentatively confirmed),
• G. Kerkyacharian (Université Pierre et Marie Curie, Paris),
• G. Lecué (CNRS and Université Paris-Est Marne-la-Vallée),
• M. Low (Wharton, University of Pennsylvania),
• A. Munk (Georg-August-Universität Göttingen),
• A. Nobel (University of North Carolina at Chapel Hill),
• S. Tsybakov (CREST-ENSAE, Paris) (tentatively confirmed) and
• M. Wegkamp (Cornell University, Ithaca) (tentatively confirmed).

The scientific committee is composed of the members of the DFG supported research group "Structural Inference in Statistics".

Structural inference in statistics consists in estimating or adaptively exploiting some partially unknown mathematical structure underlying the observed data, in order to make the inference more efficient. Certainly, elementary structural assumptions have always been at the heart of traditional statistical modeling, parametric as well as nonparametric (for instance, a simple structural model is the unknown regularity of a target function). In the recent years, increasingly elaborate and diverse forms of structure have been considered, and in this movement ideas from various other fields of mathematics incorporated into statistical thinking - for instance geometry, graph theory, signal processing, approximation theory, random matrix theory, or optimization.

Local organizers:
Gilles Blanchard, Natalie Neumeyer, Sonja Neiße, Franziska Göbel; Rui Wang-Müller

Website: http://www.mathematik.hu-berlin.de/~for1735/potsdam/