183 citations · 706 across the 60 of their papers we have counts for
12 papers · 1 filter
Near-optimal estimation of smooth transport maps with kernel sums-of-squares
Boris Muzellec, Adrien Vacher, Francis Bach +2
It was recently shown that under smoothness conditions, the squared Wasserstein distance between two distributions could be efficiently computed with appealing statistical error up…
Learning PSD-valued functions using kernel sums-of-squares
Boris Muzellec, Francis Bach, Alessandro Rudi
Shape constraints such as positive semi-definiteness (PSD) for matrices or convexity for functions play a central role in many applications in machine learning and sciences, includ…
Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a…
Infinite-Dimensional Sums-of-Squares for Optimal Control
Eloïse Berthier, Justin Carpentier, Alessandro Rudi +1
We introduce an approximation method to solve an optimal control problem via the Lagrange dual of its weak formulation. It is based on a sum-of-squares representation of the Hamilt…
Mixability made efficient: Fast online multiclass logistic regression
Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
Mixability has been shown to be a powerful tool to obtain algorithms with optimal regret. However, the resulting methods often suffer from high computational complexity which has r…
A Note on Optimizing Distributions using Kernel Mean Embeddings
Boris Muzellec, Francis Bach, Alessandro Rudi
Kernel mean embeddings are a popular tool that consists in representing probability measures by their infinite-dimensional mean embeddings in a reproducing kernel Hilbert space. Wh…
PSD Representations for Effective Probability Models
Alessandro Rudi, Carlo Ciliberto
Finding a good way to model probability densities is key to probabilistic inference. An ideal model should be able to concisely approximate any probability while being also compati…
Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
The theory of spectral filtering is a remarkable tool to understand the statistical properties of learning with kernels. For least squares, it allows to derive various regularizati…
On the Consistency of Max-Margin Losses
Alex Nowak-Vila, Alessandro Rudi, Francis Bach
The foundational concept of Max-Margin in machine learning is ill-posed for output spaces with more than two labels such as in structured prediction. In this paper, we show that th…
Online nonparametric regression with Sobolev kernels
Oleksandr Zadorozhnyi, Pierre Gaillard, Sebastien Gerschinovitz +1
In this work we investigate the variation of the online kernelized ridge regression algorithm in the setting of dimensional adversarial nonparametric regression. We derive the…
Disambiguation of weak supervision with exponential convergence rates
Vivien Cabannes, Francis Bach, Alessandro Rudi
Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete y…
Fast rates in structured prediction
Vivien Cabannes, Alessandro Rudi, Francis Bach
Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, betwee…