David Lopez-Paz

According to our database, David Lopez-Paz authored at least 52 papers between 2011 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Unified Uncertainty Calibration.

Kamalika Chaudhuri, David Lopez-Paz

CoRR, 2023

Discovering environments with XRM.

Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz

CoRR, 2023

Context is Environment.

Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja

CoRR, 2023

A Closer Look at In-Context Learning under Distribution Shifts.

Kartik Ahuja, David Lopez-Paz

CoRR, 2023

Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization.

Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz

Proceedings of the International Conference on Machine Learning, 2023

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz

Proceedings of the International Conference on Machine Learning, 2023

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations.

Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Interpolation consistency training for semi-supervised learning.

Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz

Neural Networks, 2022

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs.

Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag

J. Mach. Learn. Res., 2022

Recycling diverse models for out-of-distribution generalization.

Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz

CoRR, 2022

Measuring and signing fairness as performance under multiple stakeholder distributions.

David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier

CoRR, 2022

Throwing Away Data Improves Worst-Class Error in Imbalanced Classification.

Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz

CoRR, 2022

Rich Feature Construction for the Optimization-Generalization Dilemma.

Jianyu Zhang, David Lopez-Paz, Léon Bottou

Proceedings of the International Conference on Machine Learning, 2022

Simple data balancing achieves competitive worst-group-accuracy.

Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

What classifiers know what they don't?

Mohamed Ishmael Belghazi, David Lopez-Paz

CoRR, 2021

Linear unit-tests for invariance discovery.

Benjamin Aubin, Agnieszka Slowik, Martín Arjovsky, Léon Bottou, David Lopez-Paz

CoRR, 2021

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers.

Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

In Search of Lost Domain Generalization.

Ishaan Gulrajani, David Lopez-Paz

Proceedings of the 9th International Conference on Learning Representations, 2021

Using Hindsight to Anchor Past Knowledge in Continual Learning.

Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations.

Jean-Rémi King, François Charton, David Lopez-Paz, Maxime Oquab

NeuroImage, 2020

Permutation Equivariant Models for Compositional Generalization in Language.

Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Invariant Risk Minimization.

Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz

CoRR, 2019

Learning about an exponential amount of conditional distributions.

Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz

CoRR, 2019

Single-Model Uncertainties for Deep Learning.

Natasa Tagasovska, David Lopez-Paz

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning about an exponential amount of conditional distributions.

Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Interpolation Consistency Training for Semi-supervised Learning.

Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Manifold Mixup: Better Representations by Interpolating Hidden States.

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio

Proceedings of the 36th International Conference on Machine Learning, 2019

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension.

Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz

Proceedings of the 36th International Conference on Machine Learning, 2019

Non-linear Causal Inference Using Gaussianity Measures.

Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

Proceedings of the Cause Effect Pairs in Machine Learning, 2019

2018

Frequentist uncertainty estimates for deep learning.

Natasa Tagasovska, David Lopez-Paz

CoRR, 2018

Adversarial Vulnerability of Neural Networks Increases With Input Dimension.

Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz

CoRR, 2018

Optimizing the Latent Space of Generative Networks.

Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam

Proceedings of the 35th International Conference on Machine Learning, 2018

mixup: Beyond Empirical Risk Minimization.

Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz

Proceedings of the 6th International Conference on Learning Representations, 2018

Causal Discovery Using Proxy Variables.

Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz

Proceedings of the 6th International Conference on Learning Representations, 2018

Easing non-convex optimization with neural networks.

David Lopez-Paz, Levent Sagun

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Gradient Episodic Memory for Continuum Learning.

David Lopez-Paz, Marc'Aurelio Ranzato

CoRR, 2017

Patient-Driven Privacy Control through Generalized Distillation.

Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel

Proceedings of the IEEE Symposium on Privacy-Aware Computing, 2017

Gradient Episodic Memory for Continual Learning.

David Lopez-Paz, Marc'Aurelio Ranzato

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Revisiting Classifier Two-Sample Tests.

David Lopez-Paz, Maxime Oquab

Proceedings of the 5th International Conference on Learning Representations, 2017

Discovering Causal Signals in Images.

David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Geometrical Insights for Implicit Generative Modeling.

Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab

Proceedings of the Braverman Readings in Machine Learning. Key Ideas from Inception to Current State, 2017

2016

Non-linear Causal Inference using Gaussianity Measures.

Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez

J. Mach. Learn. Res., 2016

Minimax Lower Bounds for Realizable Transductive Classification.

Ilya O. Tolstikhin, David Lopez-Paz

CoRR, 2016

Unifying distillation and privileged information.

David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik

Proceedings of the 4th International Conference on Learning Representations, 2016

No Regret Bound for Extreme Bandits.

Robert Nishihara, David Lopez-Paz, Léon Bottou

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

The Randomized Causation Coefficient.

David Lopez-Paz, Krikamol Muandet, Benjamin Recht

J. Mach. Learn. Res., 2015

Towards a Learning Theory of Cause-Effect Inference.

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Randomized Nonlinear Component Analysis.

David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

The Randomized Dependence Coefficient.

David López-Paz, Philipp Hennig, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Gaussian Process Vine Copulas for Multivariate Dependence.

David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Semi-Supervised Domain Adaptation with Non-Parametric Copulas.

David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

DipTools: experimental data visualization tool for the DipGame testbed.

Angela Fabregues, David López-Paz, Carles Sierra

Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), 2011