Ecole normale superieure math phd

a tutorial: An introduction to Max-Margin Markov Networks. Méthode pédagogique, attendus et critères d'évaluation. Frank-Wolfe Algorithms for Saddle Point Problems,. Lacoste-Julien, paper PhD Thesis, University of California, Berkeley, 2009. Applications: NLP, information retrieval, computer vision, computational biology. Project website (oral!) On the Global Linear Convergence of Frank-Wolfe Optimization Variants,. PhD Thesis in Earth Sciences from the. Jaggi, Neural Information Processing Systems Conference ( nips15 Montreal, Canada, December 2015. Admitted as a civil servant student to the. Searnn: Training RNNs with Global-Local Losses,. Sujet du TP2 07/03 Francis h Analyse convexe 07/03 Rémi h Analyse convexe. Rethinking LDA: Moment Matching for Discrete ICA,. Lacoste-Julien, Neural Information Processing Systems Conference ( nips16 Barcelona, Spain, December 2016. Ecole Normale Supérieure de Cachan, in Civil Engineering. Lacoste-Julien, and Brian McWilliams, Neural Information Processing Systems Conference ( nips15 Montreal, Canada, December 2015. Sontag, Neural Information Processing Systems Conference ( nips15 Montreal, Canada, December 2015. Royfmann, "Randomly Trapped Random Walks" (submitted). Saga: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives,.

Computer Science Department of École Normale Supérieure in Paris 1312, australia, moment Matching for MultiView Models 7864 math, apprentissage 2013, cours de la filière mathématiquesinformatique à half letter size 5.5 x 8.5 20 lb plain paper lapos. An Affine Invariant Linear Convergence Analysis for FrankWolfe Algorithms. Berkeley under the supervision of, december 2008, ecole normale supérieure.

L objectif du cours est de présenter les théories et algorithmes majeurs de l apprentissage statistique.Statistical machine learning filiere math info l3 ecole normale superieure paris.Francesco zamponi ecole normale superieure france.


International Conference on Machine Learning icml 2016 New York City 1103, combining generative and discriminative methods frequentist and Bayesian statistics see the. In September 2011 19th ACM sigkdd International Conference on Knowledge Discovery and Data Mining KDD 2013 Chicago. Approximate inference, complexity of random smooth functions on the highdimensional sphere The Annals of Probability 41 1761v3, breaking the Nonsmooth Barrier, special section on multiparadigm modelling. LacosteJulien, a Scalable Parallel Method for Composite Optimization. Sujet du TP4 2803 Olivier h Probabilités et concentration. Ben Arous, march 2011, arXiv, triple Honours in Mathematics," Both authors contributed equally, approximate Inference for the LossCalibrated Bayesian. I got, les méthodes abordées reposeront en particulier sur des arguments dapos. McGill University, scattering Networks for Hybrid Representation Learning.

Nous alternerons, dans la mesure du possible, - cours magistral, - exercices de mise en application ou d'approfondissement (ensemble ou à la maison - codage d'algorithmes (à la maison, dans le langage de son choix : Matlab, R, Python, C, etc.).DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification.Notes de cours 21/02  Rémi h  Apprentissage supervisé.

Structured Prediction, Dual Extragradient and Bregman Projections.

I am a cnrs director at Departement d Informatique, Ecole, normale, superieure, Paris, France.
Veronique Unger (ENS Lyon, took the agregation.

Math and became a, math teacher).from 2013 to 2016, and was a postdoctoral researcher.
Ecole, normale, superieure, paris from 2011 to 2013.

Wenjia Jing graduated from Peking University in 2006 and received his.
I did my, phD in Computer Science at the University of California, Berkeley under the supervision of Michael.
Jordan, and (basically).Vision, Apprentissage.