183 citations · 706 across the 60 of their papers we have counts for
6 papers · 1 filter
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco +1
In this paper, we propose and study a Nyström based approach to efficient large scale kernel principal component analysis (PCA). The latter is a natural nonlinear extension of clas…
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regr…
A General Theory for Structured Prediction with Smooth Convex Surrogates
Alex Nowak-Vila, Francis Bach, Alessandro Rudi
In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based o…
Efficient online learning with kernels for adversarial large scale problems
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
We are interested in a framework of online learning with kernels for low-dimensional but large-scale and potentially adversarial datasets. We study the computational and theoretica…
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions
Dmitrii Ostrovskii, Alessandro Rudi
In this work we provide an estimator for the covariance matrix of a heavy-tailed multivariate distributionWe prove that the proposed estimator admits an \tex…
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance
Ulysse Marteau-Ferey, Dmitrii Ostrovskii, Francis Bach +1
We consider learning methods based on the regularization of a convex empirical risk by a squared Hilbertian norm, a setting that includes linear predictors and non-linear predictor…