260 citations · 836 across the 69 of their papers we have counts for
8 papers · 1 filter
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
Grégoire Mialon, Alexandre d'Aspremont, Julien Mairal
We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce…
Optimal Complexity and Certification of Bregman First-Order Methods
Radu-Alexandru Dragomir, Adrien Taylor, Alexandre d'Aspremont +1
We provide a lower bound showing that the convergence rate of the NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions satisfying the -smoothne…
Polyak Steps for Adaptive Fast Gradient Method
Mathieu Barré, Alexandre d'Aspremont
Accelerated algorithms for minimizing smooth strongly convex functions usually require knowledge of the strong convexity parameter . In the case of an unknown , current adapt…
Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi
Given a measurement graph and an unknown signal , we investigate algorithms for recovering from pairwise measurements of the form ; $\…
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport
François-Pierre Paty, Alexandre d'Aspremont, Marco Cuturi
Estimating Wasserstein distances between two high-dimensional densities suffers from the curse of dimensionality: one needs an exponential (wrt dimension) number of samples to ensu…
Naive Feature Selection: a Nearly Tight Convex Relaxation for Sparse Naive Bayes
Armin Askari, Alexandre d'Aspremont, Laurent El Ghaoui
Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of n…