David Lopez-Paz

I am a research scientist at Facebook AI Research in Paris, France. Here is my email and public key.


Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio
arXiv, 2018
Frequentist uncertainty estimates for deep learning
Natasa Tagasovska, David Lopez-Paz
arXiv, 2018
SAM: Structural Agnostic Model, causal discovery and penalized adversarial learning
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
arXiv, 2018 (code)
Optimizing the latent space of generative networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
ICML, 2018
Adversarial vulnerability of neural networks increases with input dimension
Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz
arXiv, 2018
Geometrical insights for implicit generative modeling
Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
Springer's Braverman Readings in Machine Learning, 2018
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
ICLR, 2018 (code)
Easing non-convex optimization with neural networks
David Lopez-Paz, Levent Sagun
ICLR Workshops, 2018 (code)
Causal discovery using proxy variables
Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
ICLR Workshops, 2018
Learning Functional Causal Models with Generative Neural Networks
Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
Springer's Explainable and Interpretable Models in CV and ML, 2018 (code)
Gradient episodic memory for continuum learning
David Lopez-Paz, Marc'Aurelio Ranzato
NeurIPS, 2017 (code)
Patient-driven privacy control through generalized distillation
Berkay Celik, David Lopez-Paz, Patrick McDaniel
IEEE PAC, 2017
Revisiting classifier two-sample tests
David Lopez-Paz, Maxime Oquab
ICLR, 2017 (code)
Discovering causal signals in images
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
CVPR, 2017
From dependence to causation
David Lopez-Paz
Doctoral dissertation, 2016
Causal and statistical learning
Bernhard Schölkopf, Dominik Janzing, David Lopez-Paz
Oberwolfach Reports, 2016
Unifying distillation and privileged information
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
ICLR, 2016 (code)
Minimax lower bounds for realizable transductive classification
Ilya Tolstikhin, David Lopez-Paz
arXiv, 2016
No regret bound for extreme bandits
Robert Nishihara, David Lopez-Paz, Léon Bottou
Non-linear causal inference using Gaussianity measures
Daniel Hernandez-Lobato, Pablo Morales Mombiela, David Lopez-Paz, Alberto Suarez
JMLR, 2016 (code)
The randomized causation coefficient
David Lopez-Paz, Krikamol Muandet, Benjamin Recht
JMLR, 2015 (code, talk)
Towards a learning theory of cause-effect inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya Tolstikhin
ICML, 2015 (code)
Randomized nonlinear component analysis
David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schölkopf
ICML, 2014 (code, talk)
Two numerical models of Saturn rings temperature as measured by Cassini
Nicolas Altobelli, David Lopez-Paz et al.
Icarus, 2014
The randomized dependence coefficient
David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
NeurIPS, 2013 (code, talk)
Gaussian process vine copulas for multivariate dependence
David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani
ICML, 2013 (code, talk)
Semisupervised domain adaptation with nonparametric copulas
David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
NeurIPS, 2012 (code, talk)


Statistical causal learning
Bernhard Schölkopf, Ilya Tolstikhin, David Lopez-Paz
Lake Como School of Advanced Studies, 2017
David Lopez-Paz
Machine Learning Summer School, 2017
Randomized algorithms
David Lopez-Paz
Machine Learning Summer School, 2016
Kernel methods
Rohit Babbar, David Lopez-Paz, Krikamol Muandet
Machine Learning Summer School, 2015
Graphical models in computer vision
Peter Gehler, Andreas Geiger, David Lopez-Paz
University of Tübingen, 2014/15
Graphical models in computer vision
Peter Gehler, David Lopez-Paz
University of Tübingen, 2013/14
Random projections
David Lopez-Paz, David Duvenaud,
University of Cambridge, 2014


Causal learning
Martin Arjovsky, Christina Heinze-Deml, Anna Klimovskaia, Maxime Oquab, Léon Bottou, David Lopez-Paz
NeurIPS, 2018
The theory of generative adversarial networks
Sebastian Nowozin, David Lopez-Paz
DALI, 2017 (videos)
Workshop on adversarial training
David Lopez-Paz, Alec Radford, Léon Bottou
NeurIPS, 2016
Second machine learning in Cambridge meeting
David Lopez-Paz, Sebastian Nowozin, Zoubin Ghahramani
Microsoft Research Cambridge, 2015
First machine learning in Cambridge meeting
David Lopez-Paz, Sebastian Nowozin, Zoubin Ghahramani
Microsoft Research Cambridge, 2014
Workshop on randomized methods for machine learning
David Lopez-Paz, Quoc Le, Alex Smola
NeurIPS, 2013