SIERRA - 2017 - Annual activity report

SIERRA

SIERRA - 2017

Project-Team Sierra

Personnel

Overall Objectives

Statement

Research Program

Supervised Learning
Unsupervised Learning
Parsimony
Optimization

Application Domains

Application Domains

New Software and Platforms

ProxASAGA
object-states-action

New Results

On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Domain-Adversarial Training of Neural Networks
Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance
Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization
Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains
AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods
Structure-Adaptive, Variance-Reduced, and Accelerated Stochastic Optimization
Exponential convergence of testing error for stochastic gradient methods
Optimal algorithms for smooth and strongly convex distributed optimization in networks
Stochastic Composite Least-Squares Regression with convergence rate O(1/n)
Sharpness, Restart and Acceleration
PAC-Bayes and Domain Adaptation
Kernel Square-Loss Exemplar Machines for Image Retrieval
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach
Integration Methods and Accelerated Optimization Algorithms
GANs for Biological Image Synthesis
Nonlinear Acceleration of Stochastic Algorithms
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
Frank-Wolfe Algorithms for Saddle Point Problems
Convex optimization over intersection of simple sets: improved convergence rate guarantees via an exact penalty approach
A Generic Approach for Escaping Saddle points
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods
Combinatorial Penalties: Which structures are preserved by convex relaxations?
On the Consistency of Ordinal Regression Methods
Iterative hard clustering of features
Asaga: Asynchronous Parallel Saga
Sparse Accelerated Exponential Weights

Bilateral Contracts and Grants with Industry

Bilateral Contracts with Industry
Bilateral Grants with Industry

Partnerships and Cooperations

National Initiatives
European Initiatives
International Initiatives
International Research Visitors

Dissemination

Promoting Scientific Activities
Teaching - Supervision - Juries
Popularization

Bibliography

Publications of the year

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2017 Project-Team Activity Report

SIERRA

Statistical Machine Learning and Parsimony

Research centre: Paris

In partnership with: CNRS, Ecole normale supérieure de Paris

In collaboration with: Département d'Informatique de l'Ecole Normale Supérieure

Field: Applied Mathematics, Computation and Simulation

Theme: Optimization, machine learning and statistical methods

Keywords:

Computer Science and Digital Science:

A1.2.8. - Network security
A3.4. - Machine learning and statistics
A5.4. - Computer vision
A6.2. - Scientific Computing, Numerical Analysis & Optimization
A7.1. - Algorithms
A8.2. - Optimization
A9.2. - Machine learning

Other Research Topics and Application Domains:

B9.4.5. - Data science