84 citations · 232 across the 11 of their papers we have counts for
8 papers · 1 filter
Invariant Risk Minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani +1
We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions. To achieve this goal, IRM learns a da…
Single-Model Uncertainties for Deep Learning
Natasa Tagasovska, David Lopez-Paz
We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (S…
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…
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,…