43 citations · 108 across the 5 of their papers we have counts for
6 papers
Causal Discovery Using Proxy Variables
Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two r…
From Dependence to Causation
David Lopez-Paz
Machine learning is the science of discovering statistical dependencies in data, and the use of those dependencies to perform predictions. During the last decade, machine learning…
Minimax Lower Bounds for Realizable Transductive Classification
Ilya Tolstikhin, David Lopez-Paz
Transductive learning considers a training set of labeled samples and a test set of unlabeled samples, with the goal of best labeling that particular test set. Conversely,…
Towards a Learning Theory of Cause-Effect Inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf +1
We pose causal inference as the problem of learning to classify probability distributions. In particular, we assume access to a collection , where each …
Gaussian Process Vine Copulas for Multivariate Dependence
David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani
Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations e…
Semi-Supervised Domain Adaptation with Non-Parametric Copulas
David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a p…