12 citations · 51 across the 15 of their papers we have counts for
14 papers · 1 filter
Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
Andrea Basteri, Carlo Ciliberto, Alessandro Rudi
Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the…
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study und…
Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj +1
Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of…
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…
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…
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…