183 citations · 706 across the 60 of their papers we have counts for
7 papers · 2 filters
Massively scalable Sinkhorn distances via the Nyström method
Jason Altschuler, Francis Bach, Alessandro Rudi +1
The Sinkhorn "distance", a variant of the Wasserstein distance with entropic regularization, is an increasingly popular tool in machine learning and statistical inference. However,…
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…
Manifold Structured Prediction
Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi +1
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite,…
Localized Structured Prediction
Carlo Ciliberto, Francis Bach, Alessandro Rudi
Key to structured prediction is exploiting the problem structure to simplify the learning process. A major challenge arises when data exhibit a local structure (e.g., are made by "…
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Giulia Luise, Alessandro Rudi, Massimiliano Pontil +1
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkh…
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
Junhong Lin, Alessandro Rudi, Lorenzo Rosasco +1
In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We inve…