Roman Vershynin | Slides of presentations
- Analysis of random measurements.
"Sparse Representations and High Dimensional Geometry",
IPAM, UCLA, Los Angeles, CA, 2007
- Lecture 1:
The Sparse reconstruction problem and random matrices
- Lecture 2:
Upper and lower bounds for subgaussian matrices
- Lecture 3:
Random Fourier matrices
- Commentary and Bibliography
Uncertainty principles, frames, and quantization.
Trends in Harmonic Analysis,
Strobl, Austria, 2007.
Phenomena in High Dimensions,
Samos, Greece, 2007.
Sparse representations and invertibility of random matrices,
AMS 2007 Von Neumann Symposium, Snowbird, Utah, 2007
- Compressed Sensing.
Applied Mathematics Seminar, UC Davis, 2007
- Small ball probability,
additive structure and random matrices,
DIMACS, Rutgers University, 2008
- On the role of sparsity in compressed sensing
and random matrix theory,
CAMSAP 09, Aruba, 2009
- Random matrix theory from the functional
SEAM XXVI, Georgia Tech, Atlanta, 2010
- Estimation of covariance matrices,
Probability and Geometry in High Dimensions,
Paris, France, 2010.
A slightly modified version (with a discussion of Levy flights)
is in my talk at UM Statistics.
- Non-asymptotic theory of random matrices and sparsity,
Bonn Mathematical Colloquium and Workshop on Sparsity and Computation,
Bonn, Germany, 2010
- Non-asymptotic theory of random matrices: extreme singular values,
International Congress of Mathematicians,
Hyderabad, India, 2010
- Covariance estimation and invertibility of random matrices,
joint PACM Colloquium and Analysis Seminar, Princeton, 2011.
A slightly edited version is a talk at MSU.
- Random matrices: invertibility, structure, and applications,
Canadian Mathematical Society Summer Meeting, Edmonton, Alberta, 2011.
- 9-hour course on non-asymptotic analysis of random matrices,
Summer School on Random matrices -
stochastic geometry - compressed sensing, Institut Henri Poincare, France, 2011.
- Invertibility of symmetric random matrices, Random Matrices, Bonn, Germany, 2012.
- New ways of dimension reduction? Cutting data sets into small pieces,
Statistical Learning and Data Mining, Ann Arbor, 2012.
- Random hyperplane tessellations and dimension reduction,
Phenomena in high dimensions in geometric analysis, random matrices and computational geometry,
Roscoff, France, 2012.
- Probabilistic reasoning in compresses sensing,
Journees MAS, Clermont-Ferrand, France, 2012.
- Smoothed analysis of random matrices,
Asymptotic Geometric Analysis II, Saint-Petersburg, Russia, 2013.
- Sampling and high-dimensional convex geometry,
SampTA 2013, Bremen, Germany.
- Random matrices: recent progress in theory and applications,
University of Michigan, 2013.
At this moment, only scanned notes of these lectures are available.
- Lecture 1: Covariance estimation
- Lecture 2: Smoothed analysis and invertibility
- Lecture 3: Eigenvectors
- Lecture 4: Concentration and its implications
- Delocalization of eigenvectors or random matrices,
AMS Special Session on Random Matrices, Baltimore, 2014.