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Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory
Organized by: Peter L. Bartlett (1), Vladimir Koltchinskii (2), Alexandre B. Tsybakov (3) and Sara van de Geer (4)(1) Department of Statistics, University of California, 367 Evans Hall, CA 94720-3860, BERKELEY, UNITED STATES
(2) School of Mathematics, Georgia Institute of Technology, 686 Cherry Street, GA 30332-0160, ATLANTA, UNITED STATES
(3) CREST, Timbre J340, 3, av. P. Larousse, F-92240, MALAKOFF CEDEX, FRANCE
(4) Seminar fuer Statistik, ETH Zentrum, LEO D11, 8092, ZÜRICH, SWITZERLAND
The statistical analysis of high dimensional data requires new techniques, extending results from nonparametric statistics, analysis, probability, approximation theory, and theoretical computer science. The main problem is how to unveil, (or to mimic performance of) sparse models for the data. Sparsity is generally meant in terms of the number of variables included, but may also be described in terms of smoothness, entropy, or geometric structures. A key objective is to adapt to unknown sparsity, yet keeping computational feasibility.
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