Young researchers’ workshop on

Statistics, Learning and Variational Methods in Imaging

  1. Christoph Brune (UCLA)
    Learning and Experimental Design of Inverse Problems with Sparsity Constraints

  2. Slides

  3. Miguel Colom & Marc Lebrun (ENS Cachan)
    Estimating noise from (just) a single image

  4. Slides

  5. Masoumeh Dashti (University of Sussex)
    Bayesian inverse problems for functions

  6. Klaus Frick (University of Göttingen)

  7. Locally Adaptive Regression and Likelihood- Ratio Statistics

  8. Jörg Kappes (University of Heidelberg)
    Efficient MAP-Inference on Discrete Graphical Models by Polyhedral Optimization

  9. Slides

  10. Pawan Kumar (Ecole Centrale Paris and INRIA-Saclay)
    Modeling Latent Variable Uncertainty for Loss- based Learning

  11. Slides

  12. Sebastian Nowozin (Microsoft Research Cambridge)
    Efficient Non-Parametric Random Field Models for Computer Vision

  13. Stefano Pedemonte (UCL)
    Steady-state model of the radio- pharmaceutical uptake for MR-PET

  14. Slides

  15. Richard Samworth (University of Cambridge)
    High-dimensional variable selection in Statistics

  16. Slides

  17. Simon Setzer (Saarland University)
    Nonlinear eigenproblems for high-dimensional data analysis

  18. Slides

  19. Ganesh Sundaramoorthi (KAUST)
    A Viewpoint Invariant Image Representation for Visual Recognition