37 citations · 43 across the 5 of their papers we have counts for
6 papers
Semi-Supervised Learning with IPM-based GANs: an Empirical Study
Tom Sercu, Youssef Mroueh
We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised…
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu +2
We propose a new Integral Probability Metric (IPM) between distributions: the Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions for functions (critic)…
Fisher GAN
Youssef Mroueh, Tom Sercu
Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore differe…
Random Maxout Features
Youssef Mroueh, Steven Rennie, Vaibhava Goel
In this paper, we propose and study random maxout features, which are constructed by first projecting the input data onto sets of randomly generated vectors with Gaussian elements,…
Convex Learning of Multiple Tasks and their Structure
Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio +1
Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is…
q-ary Compressive Sensing
Youssef Mroueh, Lorenzo Rosasco
We introduce q-ary compressive sensing, an extension of 1-bit compressive sensing. We propose a novel sensing mechanism and a corresponding recovery procedure. The recovery propert…