In cooperation with
Conference on Structural Inference in Statistics
September 17 to 19, 2013
Photo: Karla Fritze
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 (slides avialable) by
Confirmed invited speakers are
- S. Dasgupta (University of California, San Diego) on "Learning with minimal supervision"
(download slides 1st day, slides 2nd day, slides 3rd day), and
- S. Mallat (École normale supérieure, Paris) on "High Dimensional Classification with Deep Scattering Networks"
- J. Jin (Carnegie Mellon University), Fast Network Community Detection by SCORE
- G. Kerkyacharian (Université Pierre et Marie Curie, Paris), From statistical estimation to the construction of wavelet, in a geometrical framework: The heat kernel point of view.
- G. Lecué (CNRS and Université Paris-Est Marne-la-Vallée), Learning sub-Gaussian classes: upper and minimax bounds.
- A. Munk (Georg August University Göttingen and Max Planck Institute for Biophysical Chemistry), Multiscale Change Point Inference
- A. Nobel (University of North Carolina at Chapel Hill), Large Average Submatrices of a Gaussian Random Matrix: Landscapes and Local Optima.
- A. Tsybakov (CREST-ENSAE, Paris), Empirical entropy, minimax regret and minimax risk
Further contributed talks and posters will present recent research developments.
The detailed schedule can be downloaded here. The main conference room is building 11, Room 009 (ground floor).
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.
The scientific committee is composed of the members of the DFG supported research group "Structural Inference in Statistics".
Gilles Blanchard, Natalie Neumeyer, Sonja Neiße, Franziska Göbel; Rui Wang-Müller