All Abstracts

Stephane Boucheron: A poor man's Wilks phenomenon

Gilles Blanchard: Resampling-based confidence regions in high dimension from a non-asymptotic point of view

Albert Cohen: Matching vs. basis pursuit for approximation and learning: a comparison

Ingrid Daubechies: Convergence results and counterexamples for AdABoost and related algorithms

Nira Dyn: Two algorithms for adaptive approximation of bivariate functions by piecewise linear polynomials on triangulations 

Maya Gupta: Functional Bregman Divergence, Bayesian Estimation of Distributions, and Completely Lazy Classifiers

Lee Jones: Finite sample minimax estimation, fusion in machine learning, and overcoming the curse of dimensionality

Dominique Picard: A 'Frame-work'  in Learning Theory

Vladimir Koltchinskii: Sparse Recovery Problems in Learning Theory

Tomaso Poggio: Learning: neuroscience and engineering applications

Christoph Schwab: Elliptic PDEs with random field input -- numerical analysis of forward solvers and of goal oriented input learning

Steve Smale: Vision and learning

Ingo Steinwart: Approximation Theoretical Questions for Support Vector Machines

Vladimir Temlyakov: Universality and Lebesgue inequalities in approximation and estimation

Alessandro Verri: Regularization Algorithms for Learning

Patrick Wolfe: The Nystrom Extension and Spectral Methods in Learning: A New Algorithm for Low-Rank Approximation of Quadratic Forms

Ding-Xuan Zhou: Learnability of Gaussians with Flexible Variances