The conference on "Probability Theory and Statistics in High and Infinite Dimensions: Empirical Processes Theory and Beyond" will focus on empirical processes and other methods of high-dimensional probability and their role in a variety of problems related to statistical inference for high-dimensional data as well as to nonparametric inference. The conference will be held on June 23-25, 2014 at the Center for Mathematical Sciences, University of Cambridge, UK. The list of topics includes concentration inequalities, exponential and moment bounds for Gaussian, empirical and other related processes, nonasymptotic bounds for random matrices, complexity penalization methods and oracle inequalities in high-dimensional inference, sparse recovery and low rank matrix recovery, high-dimensional problems in machine learning, nonparametric estimation and hypotheses testing, Bayesian nonparametrics.
High-dimensional probability and statistics have had impact far beyond mathematical sciences. The methods of high-dimensional probability have been crucial in understanding the performance of new machine learning algorithms and in the design of contemporary methods of analysis of big data. Bringing together researchers who have made and are currenly making important contributions to this area would facilitate the exchange of ideas leading to much better understanding of the role of high-dimensional phenomena in statistical inference and machine learning. The conference would be especially beneficial for junior participants entering these research areas. This award will support the participation of approximately twenty US-based participants in the conference.
Conference web site: www.statslab.cam.ac.uk/~nickl/Site/2014.html
