32 citations · 116 across the 12 of their papers we have counts for
12 papers
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points. A reformulation of the same GP posterior highlights that this compl…
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Giacomo Meanti, Luigi Carratino, Ernesto De Vito +1
Kernel methods provide a principled approach to nonparametric learning. While their basic implementations scale poorly to large problems, recent advances showed that approximate so…
Mean Nyström Embeddings for Adaptive Compressive Learning
Antoine Chatalic, Luigi Carratino, Ernesto De Vito +1
Compressive learning is an approach to efficient large scale learning based on sketching an entire dataset to a single mean embedding (the sketch), i.e. a vector of generalized mom…
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
Luigi Carratino, Stefano Vigogna, Daniele Calandriello +1
We introduce ParK, a new large-scale solver for kernel ridge regression. Our approach combines partitioning with random projections and iterative optimization to reduce space and t…
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
Marco Rando, Luigi Carratino, Silvia Villa +1
Gaussian process optimization is a successful class of algorithms(e.g. GP-UCB) to optimize a black-box function through sequential evaluations. However, for functions with continuo…
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…
On Mixup Regularization
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton +1
Mixup is a data augmentation technique that creates new examples as convex combinations of training points and labels. This simple technique has empirically shown to improve the ac…
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are one of the most successful frameworks to model uncertainty. However, GP optimization (e.g., GP-UCB) suffers from major scalability issues. Experimental…
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are a well studied Bayesian approach for the optimization of black-box functions. Despite their effectiveness in simple problems, GP-based algorithms hardly…
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi, Daniele Calandriello, Luigi Carratino +1
Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adap…
Learning with SGD and Random Features
Luigi Carratino, Alessandro Rudi, Lorenzo Rosasco
Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms. In this paper, we investigate their applicati…
FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
Kernel methods provide a principled way to perform non linear, nonparametric learning. They rely on solid functional analytic foundations and enjoy optimal statistical properties.…