183 citations · 706 across the 60 of their papers we have counts for
12 papers · 1 filter
Finding Global Minima via Kernel Approximations
Alessandro Rudi, Ulysse Marteau-Ferey, Francis Bach
We consider the global minimization of smooth functions based solely on function evaluations. Algorithms that achieve the optimal number of function evaluations for a given precisi…
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Vivien Cabannes, Loucas Pillaud-Vivien, Francis Bach +1
As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of…
Consistent Structured Prediction with Max-Min Margin Markov Networks
Alex Nowak-Vila, Francis Bach, Alessandro Rudi
Max-margin methods for binary classification such as the support vector machine (SVM) have been extended to the structured prediction setting under the name of max-margin Markov ne…
Non-parametric Models for Non-negative Functions
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Linear models have shown great effectiveness and flexibility in many fields such as machine learning, signal processing and statistics. They can represent rich spaces of functions…
Learning Output Embeddings in Structured Prediction
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means…
Structured and Localized Image Restoration
Thomas Eboli, Alex Nowak-Vila, Jian Sun +3
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning. We optimize a penalized energy functi…
Kernel methods through the roof: handling billions of points efficiently
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco +1
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems, since naïve implementations scale poor…
Interpolation and Learning with Scale Dependent Kernels
Nicolò Pagliana, Alessandro Rudi, Ernesto De Vito +1
We study the learning properties of nonparametric ridge-less least squares. In particular, we consider the common case of estimators defined by scale dependent kernels, and focus o…
Efficient improper learning for online logistic regression
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
We consider the setting of online logistic regression and consider the regret with respect to the 2-ball of radius B. It is known (see [Hazan et al., 2014]) that any proper algorit…
Structured Prediction with Partial Labelling through the Infimum Loss
Vivien Cabannes, Alessandro Rudi, Francis Bach
Annotating datasets is one of the main costs in nowadays supervised learning. The goal of weak supervision is to enable models to learn using only forms of labelling which are chea…
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more…
Statistical Limits of Supervised Quantum Learning
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi +1
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the boun…