David Lopez-Paz

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 58

Top Topics

Episodic Memory

Top Venues

Publications

Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
Context is Environment. ICLR (2024)
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve
Better & Faster Large Language Models via Multi-token Prediction. CoRR (2024)
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. ICLR (2023)
Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
Context is Environment. CoRR (2023)
Kamalika Chaudhuri, David Lopez-Paz
Unified Uncertainty Calibration. CoRR (2023)
Kartik Ahuja, David Lopez-Paz
A Closer Look at In-Context Learning under Distribution Shifts. CoRR (2023)
Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz
Why does Throwing Away Data Improve Worst-Group Error? ICML (2023)
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization. ICML (2023)
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
Discovering environments with XRM. CoRR (2023)
Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR (2022)
Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz
Interpolation consistency training for semi-supervised learning. Neural Networks 145 (2022)
Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. J. Mach. Learn. Res. 23 (2022)
Jianyu Zhang, David Lopez-Paz, Léon Bottou
Rich Feature Construction for the Optimization-Generalization Dilemma. CoRR (2022)
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz
Recycling diverse models for out-of-distribution generalization. CoRR (2022)
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
Simple data balancing achieves competitive worst-group-accuracy. CLeaR (2022)
Jianyu Zhang, David Lopez-Paz, Léon Bottou
Rich Feature Construction for the Optimization-Generalization Dilemma. ICML (2022)
David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier
Measuring and signing fairness as performance under multiple stakeholder distributions. CoRR (2022)
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. CoRR (2022)
Benjamin Aubin, Agnieszka Slowik, Martín Arjovsky, Léon Bottou, David Lopez-Paz
Linear unit-tests for invariance discovery. CoRR (2021)
Mohamed Ishmael Belghazi, David Lopez-Paz
What classifiers know what they don't? CoRR (2021)
Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
Simple data balancing achieves competitive worst-group-accuracy. CoRR (2021)
Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. NeurIPS (2021)
Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz
Using Hindsight to Anchor Past Knowledge in Continual Learning. AAAI (2021)
Ishaan Gulrajani, David Lopez-Paz
In Search of Lost Domain Generalization. ICLR (2021)
Jean-Rémi King, François Charton, David Lopez-Paz, Maxime Oquab
Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations. NeuroImage 220 (2020)
Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt
Permutation Equivariant Models for Compositional Generalization in Language. ICLR (2020)
Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz
Using Hindsight to Anchor Past Knowledge in Continual Learning. CoRR (2020)
Ishaan Gulrajani, David Lopez-Paz
In Search of Lost Domain Generalization. CoRR (2020)
Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML (2019)
Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz
Learning about an exponential amount of conditional distributions. CoRR (2019)
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio
Manifold Mixup: Better Representations by Interpolating Hidden States. ICML (2019)
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz
Interpolation Consistency Training for Semi-Supervised Learning. CoRR (2019)
Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz
Interpolation Consistency Training for Semi-supervised Learning. IJCAI (2019)
Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz
Invariant Risk Minimization. CoRR (2019)
Natasa Tagasovska, David Lopez-Paz
Single-Model Uncertainties for Deep Learning. NeurIPS (2019)
Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz
Learning about an exponential amount of conditional distributions. NeurIPS (2019)
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
Optimizing the Latent Space of Generative Networks. ICML (2018)
David Lopez-Paz, Levent Sagun
Easing non-convex optimization with neural networks. ICLR (Workshop) (2018)
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
mixup: Beyond Empirical Risk Minimization. ICLR (Poster) (2018)
Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
Causal Discovery Using Proxy Variables. ICLR (Workshop) (2018)
Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz
Adversarial Vulnerability of Neural Networks Increases With Input Dimension. CoRR (2018)
Natasa Tagasovska, David Lopez-Paz
Frequentist uncertainty estimates for deep learning. CoRR (2018)
David Lopez-Paz, Marc'Aurelio Ranzato
Gradient Episodic Memory for Continuum Learning. CoRR (2017)
Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
Geometrical Insights for Implicit Generative Modeling. Braverman Readings in Machine Learning (2017)
Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
Causal Discovery Using Proxy Variables. CoRR (2017)
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
Discovering Causal Signals in Images. CVPR (2017)
David Lopez-Paz, Maxime Oquab
Revisiting Classifier Two-Sample Tests. ICLR (Poster) (2017)
Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
Geometrical Insights for Implicit Generative Modeling. CoRR (2017)
David Lopez-Paz, Marc'Aurelio Ranzato
Gradient Episodic Memory for Continual Learning. NIPS (2017)
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
mixup: Beyond Empirical Risk Minimization. CoRR (2017)
Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
Optimizing the Latent Space of Generative Networks. CoRR (2017)
Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel
Patient-Driven Privacy Control through Generalized Distillation. PAC (2017)
Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel
Patient-Driven Privacy Control through Generalized Distillation. CoRR (2016)
Ilya O. Tolstikhin, David Lopez-Paz
Minimax Lower Bounds for Realizable Transductive Classification. CoRR (2016)
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
Discovering Causal Signals in Images. CoRR (2016)
Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
Non-linear Causal Inference using Gaussianity Measures. J. Mach. Learn. Res. 17 (2016)
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
Unifying distillation and privileged information. ICLR (Poster) (2016)
Robert Nishihara, David Lopez-Paz, Léon Bottou
No Regret Bound for Extreme Bandits. AISTATS (2016)
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
Towards a Learning Theory of Cause-Effect Inference. ICML (2015)
Robert Nishihara, David Lopez-Paz, Léon Bottou
No Regret Bound for Extreme Bandits. CoRR (2015)
David Lopez-Paz, Krikamol Muandet, Benjamin Recht
The Randomized Causation Coefficient. J. Mach. Learn. Res. 16 (2015)
David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
Randomized Nonlinear Component Analysis. ICML (2014)
David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
Randomized Nonlinear Component Analysis. CoRR (2014)
David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf
Semi-Supervised Domain Adaptation with Non-Parametric Copulas CoRR (2013)
David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani
Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) (2013)