Nicolas Courty

Data Dependent Kernel Approximation using Pseudo Random Fourier Features

2017 · arXiv (Cornell University)

Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to the pairwise evaluations in kernel methods, the complexity of kernel computation grows as the data size increases; thus the …

Wasserstein Adversarial Regularization (WAR) on label noise

2019

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve …