Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
- A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets
- Convex Relaxation for Combinatorial Penalties
- Kernel change-point detection
- On the Equivalence between Herding and Conditional Gradient Algorithms
- -fold cross-validation and -fold penalization in least-squares density estimation
- Machine learning for Neuro-imaging
- SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
- Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
- A convex relaxation for weakly supervised classifiers
- Multi-Class Cosegmentation
- A latent factor model for highly multi-relational data
- Semi-supervised NMF with time-frequency annotations for single-channel source separation
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
- A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets
- Convex Relaxation for Combinatorial Penalties
- Kernel change-point detection
- On the Equivalence between Herding and Conditional Gradient Algorithms
- -fold cross-validation and -fold penalization in least-squares density estimation
- Machine learning for Neuro-imaging
- SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
- Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
- A convex relaxation for weakly supervised classifiers
- Multi-Class Cosegmentation
- A latent factor model for highly multi-relational data
- Semi-supervised NMF with time-frequency annotations for single-channel source separation
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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