• Amphi. Jean-Jaurès, 29 rue d'Ulm, 21~22 September 2017, 8:55~17:20

Program Schedule (Tentative)

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

  • 8:55 – 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 ———
  • 10:40 – 11:05 Jean-Philippe Vert, ENS / Mines / Curie

    Learning on the symmetric group (more…)

  • 11:05 – 11:30 Koji Tsuda, U. of Tokyo / RIKEN AIP

    Automatic design of functional molecules and materials (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 – buffet served onsite
  • 13:50 – 14:00 student talk 1: Mathieu Carrière

    Sliced Wasserstein Kernels for Persistence Diagrams (more…)

  • 14:00 – 14:10 student talk 2: Arthur Pajot

    Deep Learning for Physical Processes: An Application to Sea Surface Temperature Forecasting (more…)

  • 14:10 – 14:20 student talk 3: Adil Salim

    Convergence of a constant step stochastic proximal gradient algorithm with generalization to random monotone operators (more…)

  • 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…)

  • 8:55 – 9:20 Arnak Dalalyan, CREST, U. Paris-Saclay

    User-friendly error bounds for sampling from a strongly log-concave density (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 Irène Waldspurger, U. Paris Dauphine

    Phase retrieval with the alternating projections method (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…)

  • 11:55 – 13:45 ——— Lunch – buffet served onsite
  • 13:45 – 13:55 student talk 4: Anna Korba

    A learning theory for ranking aggregation (more…)

  • 13:55 – 14:20 Masashi Sugiyama, U. of Tokyo / RIKEN AIP

    Classification from Weak Supervision (more…)

  • 14:20 – 14:45 Robert Gower, Inria Paris

    Stochastic Variance Reduced Methods Based on Sketching and Projecting (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

    Faster independent component analysis by preconditioning with
    Hessian approximations (more…)