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6 papers · 2 filters
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Mastane Achab, Anna Korba, Stephan Clémençon
Whereas most dimensionality reduction techniques (e.g. PCA, ICA, NMF) for multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking data, viewed…
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Robin Vogel, Aurélien Bellet, Stéphan Clémençon
The performance of many machine learning techniques depends on the choice of an appropriate similarity or distance measure on the input space. Similarity learning (or metric learni…
A Structured Prediction Approach for Label Ranking
Anna Korba, Alexandre Garcia, Florence d'Alché Buc
We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two…
Autoencoding any Data through Kernel Autoencoders
Pierre Laforgue, Stephan Clémençon, Florence d'Alché-Buc
This paper investigates a novel algorithmic approach to data representation based on kernel methods. Assuming that the observations lie in a Hilbert space X, the introduced Kernel…
Greedy stochastic algorithms for entropy-regularized optimal transport problems
Brahim Khalil Abid, Robert M. Gower
Optimal transport (OT) distances are finding evermore applications in machine learning and computer vision, but their wide spread use in larger-scale problems is impeded by their h…
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, st…