Djalil Chafaï, Olivier Guédon, Guillaume Lecué and Alain Pajor just made available the presentations given at the Workshop on Probability and Geometry in High Dimensions that took place a week or so ago in Marne-la-Vallee, near Paris. From the main site, here is the list of speakers and their presentations:
- Vladimir Koltchinskii (Georgia Institute of Technology, Atlanta, USA)
Sparse Recovery in Linear Spans and Convex Hulls of Infinite Dictionaries- Jared Tanner (University of Edinburgh, Edinburgh, Scotland)
Random matrix theory and stochastic geometry in compressed sensing- Omer Friedland (Université Pierre et Marie Curie, Paris, France)
Random embedding of ell_p^n into ell_r^N- Philippe Jaming (Université d'Orléans, Orléans, France)
Some annihilating pairs in harmonic analysis- Francis Bach (INRIA & ÉNS, Paris, France)
High-Dimensional Non-Linear Variable Selection- Radosław Adamczak (University of Warsaw, Warsaw, Poland)
Geometric properties of random matrices with independent log-concave rows/columns- Leonid Pastur (Mathematical Division, Institute for Low Temperatures, Kharkov, Ukraine)
Central Limit Theorem for Linear Eigenvalue Statistics of Random Matrices with Independent Entries- Alexandre Tsybakov (CREST & Université Pierre et Marie Curie, Paris, France)
Estimation of High-Dimensional Low Rank Matrices- Rafał Latała (Warsaw University, Warsaw, Poland)
On 1-symmetric logarithmically concave distributions- Ionel Popescu (Georgia Institute of Technology, Atlanta, USA)
Random Matrices and Analyticity of the Planar Limit- Michel Talagrand (CNRS & Université Pierre et Marie Curie, Paris, France)
Are many small sets explicitely small?- Stanislaw J. Szarek (Université Pierre et Marie Curie, Paris, France)
Almost-Euclidean subspaces of ell_1^N via tensor products: a low-tech approach to randomness reduction- Holger Rauhut (Universität Bonn, Bonn, Germany)
Compressive Sensing and Structured Random Matrices- Charles Dossal (Université de Bordeaux 1, Bordeaux, France)
Support identification of sparse vectors from random noisy measurements- Franck Barthe (Université Paul-Sabatier, Toulouse, France)
Convergence of bipartite functionals in many dimensions- Ronald Devore (University of South Carolina, Columbia, USA)
Approximating and Querying Functions in High Dimensions- Shahar Mendelson (Technion, Haïfa, Israël)
On weakly bounded empirical processes- Artem Zvavitch (Kent State University, Kent, USA)
The iterations of intersection body operator- Boris Kashin (Steklov Mathematical Institute, Moscow, Russia)
On the uniform approximation of the partial sum of the Dirichlet series by a shorter sum- Keith Ball (University College London, London, Great Britain)
Noise sensitivity and Gaussian surface area- Shuheng Zhou (ETH, Zürich, Switzerland)
Thresholded Lasso for High Dimensional Variable Selection and Statistical Estimation- Krzysztof Oleszkiewicz (Warsaw University, Warsaw, Poland)
L^1-smoothing for the multi-dimensional Ornstein-Uhlenbeck semigroup- Roman Vershynin (University of Michigan, Ann Arbor, USA)
Estimation of covariance matrices- Sandrine Péché (Université de Grenoble, Grenoble, France)
The spectrum of non white sample covariance matrices- Grigoris Paouris (Texas A&M University, College Station, USA)
On the existence of a subgaussian direction on log-concave measures- Assaf Naor (Courant Institute & University of New-York, New-York, USA)
Random martingales and localization of maximal inequalities
Congratulations to the organizers for following through by posting the presentations on the site. I note that the presentation by Jared Tanner ( Random matrix theory and stochastic geometry in compressed sensing ) is easier to understand than it used to be. Maybe I am getting accustomed to the subject. Other items that jumped at my face was the bound in slide 61 of Holger Rauhut's presentation (Compressive Sensing and Structured Random Matrices): 57, I don't think I remember that number, wow, certainly if 57 is the smallest bound then this Legendre expansion thing is not going to take off in neutron transport problems. It might do a good job in atmospheric transport problems instead.
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