[~] Three minicourses on Signal Analysis and Big Data

The workshop consists of three courses on applied harmonic analysis and machine learning, given by leading experts. Each speaker will deliver four lectures. The school starts on Monday afternoon and ends on Friday morning. The late afternoon of the first four days is reserved to contributed talks. Graduated students in Mathematics, Physics, Computer Science and Engineering, as well as postdoctoral fellows and young researchers, are welcome. Financial support is available to cover accommodation expenses, on a first-come first-served basis. There is no registration fee, but participants are required to register before June 30. Those applying for financial support or to give a talk should register by June 1.

Andreas Maurer
Concentration inequalities
Abstract     Notes     Slides
Lorenzo Rosasco
Regularization: from inverse problems to large scale machine learning
Abstract     Video 1     Video 2     Video 3     Video 4    
Felix Voigtlaender
Sparsity Properties of Frames via Decomposition Spaces
Abstract     Notes     Slides

Contributed Talks

  1. Alberto A. Vergani - RS-fMRI analysis in healthy subjects confirms gender based differences
  2. Luca Oneto - Differential Privacy and Generalization: Sharper Bounds, Theoretically Grounded Algorithms, and Thresholdout
  3. Ilaria Giulini - Kernel spectral clustering
  4. Philipp Petersen - Optimal Approximation with Sparsely Connected Deep Neural Networks
  5. Lukas Sawatzki - Inhomogeneous Shearlet coorbit Spaces
  6. Diego Liberati - From time varying autoregressive spectral estimation to big data PieceWise Affine identification