This workshop will focus on the modelisation and statistical analysis of large structured data sets as appearing in modern signal processing and machine learning. Recurrent questions will be: what are "good" models of high-dimensional data which are realistic enough while remaining analytically tractable, and what are their universality properties?
 

Many modern problems (e.g. compressed sensing, community detection, PCA and tensor decomposition) seek to infer some latent signal from high-dimensional noisy data. A theoretical analysis is often challenging due to the subtle correlations and structured dependencies among the observed features. Remarkably, many of these systems exhibit universal statistics ie., similar properties as a surrogate random system. There has been substantial recent progress at the intersection of statistical physics, statistics, probability and machine learning in rigorously establishing these empirical observations, and these properties have been critically exploited for statistical learning.

This workshop will focus on these recent interdisciplinary investigations, with a view towards discovering new connections among the diverse approaches to these problems of common interest.

Call for Contributed Abstracts: All applicants are encouraged to submit an abstract for a poster presentation. Abstract templates are available below for download.

Grants: A limited number of grants are available to support the attendance of selected participants, with priority given to participants from developing countries. There is no registration fee.