France / Japan Machine Learning Workshop

Program Schedule (Tentative)

All invited talks will be 25 minutes long. Student contributed talks will be 10 minutes.

9:00 – 9:20 opening & RIKEN AIP presentation
9:20 – 9:45 Ichiro Takeuchi, NagoyaTech
Fitting and Testing Sparse High-Order Interaction Models (more…)
9:45 – 10:10 Joseph Salmon, Telecom
From safe screening rules to working sets for faster Lasso-type solvers
(more…)
10:10 – 10:40 coffee
11:05 – 11:30 Koji Tsuda, U. of Tokyo / RIKEN AIP
Automatic design of functional molecules and materials (more…)
10:40 – 11:05 Jean-Philippe Vert, ENS / Mines / Curie
Learning on the symmetric group (more…)
11:30 – 11:55 Chloé Azencott, Curie / Mines / INSERM
Network-guided high-dimensional feature selection in precision medicine (more…)
11:55 – 13:50 lunch

13:50 – 14:00 student talk 1
14:00 – 14:10 student talk 2
14:10 – 14:20 student talk 3
14:20 – 14:45 Junya Honda, U. of Tokyo / RIKEN AIP
Bandit Problems for Pairwise Feedback Models (more…)
14:45 – 15:10 Vianney Perchet, ENS Saclay
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe (more…)
15:10 – 15:40 coffee
15:40 – 16:05 Shohei Shimizu, Shiga U. / RIKEN AIP
Causal discovery and prediction mechanisms (more…)
16:05 – 16:30 Judith Rousseau, Oxford U. / U. Paris Dauphine
Using asymptotics to understand ABC (more…)
16:30 – 16:55 Kohei Hatano, Kyushu U. / RIKEN AIP
Boosting the kernelized shapelets: Theory and algorithms for local features (more…)
16:55 – 17:20 Gabriel Peyré, ENS Ulm
Optimal Transport and Deep Generative Models (more…)

9:20 – 9:45 Akiko Takeda, ISM / RIKEN AIP
Proximal DC Algorithm for Sparse Optimization (more…)
9:45 – 10:10 Francis Bach, ENS/Inria Paris
Optimal algorithms for smooth and strongly convex distributed optimization in networks (more…)
10:10 – 10:40 coffee
10:40 – 11:05 Takanori Maehara, RIKEN AIP
Stochastic Packing Integer Programming with a Few Queries (more…)
11:05 – 11:30 Robert Gower, Inria Paris
Stochastic Variance Reduced Methods Based on Sketching and Projecting (more…)
11:30 – 11:55 Taiji Suzuki, U. of Tokyo / RIKEN AIP
Generalization error bounds of deep learning by Bayesian and empirical risk minimization approaches from a kernel perspective (more…)

13:50 – 14:00 student talk 4
14:00 – 14:10 student talk 5
14:10 – 14:20 student talk 6
14:20 – 14:45 Irène Waldspurger, U. Paris Dauphine
Phase retrieval with the alternating projections method (more…)
14:45 – 15:10 Naonori Ueda, NTT CS Labs / RIKEN AIP
Spatio-temporal collective data analysis for real-time and proactive navigation (more…)
15:10 – 15:40 coffee
15:40 – 16:05 Ryota Tomioka, MSR Cambridge
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks (more…)
16:05 – 16:30 Pierre Alquier, CREST, U. Paris Saclay
Concentration of variational approximations of posterior distributions (more…)
16:30 – 16:55 Mathieu Blondel, NTT CS Labs
A Regularized Framework for Sparse and Structured Neural Attention (more…)
16:55 – 17:20 Alexandre Gramfort, Inria Saclay
Making sparse regression actually work for brain imaging: working sets and heteroscedasticity (more…)