Overall Objectives
Research Program
Application Domains
Software and Platforms
New Results
- Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
- Minimizing Finite Sums with the Stochastic Average Gradient.
- Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
- Non-strongly-convex smooth stochastic approximation with convergence rate
- Streaming Bayesian Inference
- Convex Relaxations for Permutation Problems
- Phase retrieval for imaging problems
- Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression
- Maximizing submodular functions using probabilistic graphical models
- Reflection methods for user-friendly submodular optimization
- Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs
- Large-Margin Metric Learning for Partitioning Problems
- Comparison between multi-task and single-task oracle risks in kernel ridge regression
- Sharp analysis of low-rank kernel matrix approximations
- fMRI encoding and decoding models
- Structured Penalties for Log-linear Language Models
- Distributed Large-scale Natural Graph Factorization
- Evaluating Speech Features with the Minimal-Pair ABX task
- Hidden Markov Tree Models for Semantic Class Induction
- Domain Adaptation for Sequence Labeling using Hidden Markov Models
- Simple Greedy Matching for Aligning Large Knowledge Bases
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
Overall Objectives
Research Program
Application Domains
Software and Platforms
New Results
- Block-Coordinate Frank-Wolfe Optimization for Structural SVMs
- Minimizing Finite Sums with the Stochastic Average Gradient.
- Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
- Non-strongly-convex smooth stochastic approximation with convergence rate
- Streaming Bayesian Inference
- Convex Relaxations for Permutation Problems
- Phase retrieval for imaging problems
- Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression
- Maximizing submodular functions using probabilistic graphical models
- Reflection methods for user-friendly submodular optimization
- Convex Relaxations for Learning Bounded Treewidth Decomposable Graphs
- Large-Margin Metric Learning for Partitioning Problems
- Comparison between multi-task and single-task oracle risks in kernel ridge regression
- Sharp analysis of low-rank kernel matrix approximations
- fMRI encoding and decoding models
- Structured Penalties for Log-linear Language Models
- Distributed Large-scale Natural Graph Factorization
- Evaluating Speech Features with the Minimal-Pair ABX task
- Hidden Markov Tree Models for Semantic Class Induction
- Domain Adaptation for Sequence Labeling using Hidden Markov Models
- Simple Greedy Matching for Aligning Large Knowledge Bases
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
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