141 citations · 416 across the 15 of their papers we have counts for
7 papers · 1 filter
Targeted Separation and Convergence with Kernel Discrepancies
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami +1
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distr…
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou +2
Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We…
From optimal transport to generative modeling: the VEGAN cookbook
Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin +2
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution and the latent variable model distributi…
AdaGAN: Boosting Generative Models
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet +2
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are not…
Consistent Kernel Mean Estimation for Functions of Random Variables
Carl-Johann Simon-Gabriel, Adam Ścibior, Ilya Tolstikhin +1
We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings. We show that for any continuous function , consi…
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
Carl-Johann Simon-Gabriel, Bernhard Schölkopf
Kernel mean embeddings have recently attracted the attention of the machine learning community. They map measures from some set to functions in a reproducing kernel Hilbert…
Removing systematic errors for exoplanet search via latent causes
Bernhard Schölkopf, David W. Hogg, Dun Wang +4
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired b…