2013-14: LDHD: SAMSI-CRM Workshop on Geometric Aspects of High-dimensional Inference: March 31-April 2, 2014

The workshop was held at SAMSI in Research Triangle Park, NC, and was a joint venture between SAMSI and Centre de Recerca Matemàtica (CRM) in Barcelona, Spain.  

The development of methods of statistical inference for high-dimensional data has become a focal point of research in statistics and machine learning in the recent years. One of the crucial problems is to understand how to estimate efficiently a very high-dimensional object under certain "low complexity" constraints that make the estimation possible. In specific settings, "low complexity" could mean, for instance, sparsity of a vector in a high-dimensional space or low-rank properties of a large matrix. The goal is to develop methods that would be adaptive to the underlying "low complexity" structure. Such problems are extremely important both in statistics -- for instance, in high-dimensional regression or in covariance matrix estimation -- and in a variety of applications, including compressed sensing, collaborative filtering, and quantum state tomography.

Theoretical analysis of methods of high-dimensional inference often relies on deep understanding of the underlying geometry of high-dimensional spaces that leads to highly nontrivial problems of geometric nature. Similar problems have occurred and have been studied in such areas of mathematics as high-dimensional probability, random matrix theory, asymptotic geometric analysis, convex geometry, and additive combinatorics. Some of the tools developed in these areas proved to be extremely useful in high-dimensional statistics; these tools include empirical processes methods, concentration inequalities, and various techniques from the theory of random matrices. There is also great potential for applications of other tools developed in recent years, such as generic chaining bounds for stochastic processes or an emerging theory of log-concave distributions in high-dimensional spaces.

The goal of this workshop was to bring together researchers actively working on the development of high-dimensional inference in statistics, machine learning, compressed sensing, and other related areas with mathematicians who made major contributions to high-dimensional probability and asymptotic geometric analysis in recent years. This provided a great opportunity for fruitful discussions of cutting edge problems in high-dimensional statistics and major advances in our understanding of geometry of high-dimensional spaces.

Topics covered by the workshop may included:

• low rank matrix estimation;
• sparse recovery and compressed sensing;
• covariance estimation for high-dimensional data;
• model selection and oracle inequalities in high-dimensional statistics;
• statistical inference for log-concave distributions in high dimensions;
• non-asymptotic theory of random matrices;
• concentration inequalities, generic chaining, and empirical processes methods in high-dimensional statistics;
• hypotheses testing for high-dimensional objects.

If you need further information please send an email to [email protected] .

Schedule
Participant List
Speaker Titles and Abstracts
Poster Titles

Schedule

Monday, March 31, 2014
at SAMSI, Room 150

Tuesday, April 1, 2014
at SAMSI, Room 150

Wednesday, April 2, 2014
at SAMSI, Room 150