Convex Relaxation Methods for Geometric Problems in Scientific Computing

February 11 - 15, 2013

Schedule


Monday, February 11, 2013

9:00 - 9:50
Francis Bach (Institut National de Recherche en Informatique Automatique (INRIA))

Structured Sparsity-inducing Norms through Submodular Functions
PDF Presentation

10:15 - 11:05
Massimiliano Pontil (University College London)

Multi-task Learning and Structured Sparsity
PDF Presentation

11:30 - 12:20
Gabriele Steidl (Universität Kaiserslautern)

Constrained image restoration problems
PDF Presentation

2:30 - 3:20
4:00 - 4:50

Tuesday, February 12, 2013

9:00 - 9:50
Xavier Bresson (City University of Hong Kong)

Total Variation Clustering
PDF Presentation

10:15 - 11:05
Thomas Pock (Technische Universität Graz)

Convex relaxation of a class of vertex penalizing functionals

11:30 - 12:20
2:30 - 3:20
4:00 - 4:50

Wednesday, February 13, 2013

9:00 - 9:50
Daniel Cremers (Technische Universtitat München)

Convex Relaxations for the Mumford-Shah Functional
PDF Presentation

10:15 - 11:05
11:30 - 12:20
2:30 - 3:20
Stanley Osher (University of California, Los Angeles (UCLA))

Inverse scale space, linearized Bregman and sparse learning

4:00 - 4:50

Thursday, February 14, 2013

9:00 - 9:50
Antonin Chambolle (École Polytechnique)

Functional lifting for scalar and vector-valued maps

10:15 - 11:05
Gabriel Peyre (Université de Paris IX (Paris-Dauphine))

SURE-based Parameter Selection for Sparse Regularization of Inverse Problems
PDF Presentation

11:30 - 12:20
Wotao Yin (Rice University)

Block multi-convex optimization
PDF Presentation

2:30 - 3:20
Christoph Schnörr (University of Heidelberg)

Shape, Segmentation and Convex Relaxations in Computer Vision

4:00 - 4:50
Evgeny Strekalovskiy (Technical University of Munich)

Total Cyclic Variation
PDF Presentation


Friday, February 15, 2013

9:00 - 9:50
Mila Nikolova (École Normale Supérieure de Cachan)

ℓ1-concave versus ℓ1 - TV energies. Questions and challenges
PDF Presentation

10:15 - 11:05
Luminita Vese (University of California, Los Angeles (UCLA))

Convex and nonconvex variational models for image segmentation and restoration