Lopez-Paz, David

40 publications

NeurIPS 2025 From Bytes to Ideas: Language Modeling with Autoregressive U-Nets Mathurin Videau, Badr Youbi Idrissi, Alessandro Leite, Marc Schoenauer, Olivier Teytaud, David Lopez-Paz
ICLR 2025 The Pitfalls of Memorization: When Memorization Hurts Generalization Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, Pascal Vincent
ICLRW 2025 Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification Konstantin Donhauser, Charles Arnal, Mohammad Pezeshki, Vivien Cabannes, David Lopez-Paz, Kartik Ahuja
ICML 2024 Better & Faster Large Language Models via Multi-Token Prediction Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, David Lopez-Paz, Gabriel Synnaeve
ICLR 2024 Context Is Environment Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
ICML 2024 Discovering Environments with XRM Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
NeurIPSW 2024 The Pitfalls of Memorization: When Memorization Hinders Generalization Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, Pascal Vincent
ICMLW 2023 A Closer Look at In-Context Learning Under Distribution Shifts Kartik Ahuja, David Lopez-Paz
NeurIPSW 2023 Context Is Environment Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja
NeurIPSW 2023 Context Is Environment Sharut Gupta, David Lopez-Paz, Stefanie Jegelka, Kartik Ahuja
NeurIPSW 2023 Discovering Environments with XRM Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
ICLR 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
ICML 2023 Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization Alexandre Rame, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Leon Bottou, David Lopez-Paz
ICML 2023 Why Does Throwing Away Data Improve Worst-Group Error? Kamalika Chaudhuri, Kartik Ahuja, Martin Arjovsky, David Lopez-Paz
NeurIPSW 2022 Pre-Train, Fine-Tune, Interpolate: A Three-Stage Strategy for Domain Generalization Alexandre Rame, Jianyu Zhang, Leon Bottou, David Lopez-Paz
ICML 2022 Rich Feature Construction for the Optimization-Generalization Dilemma Jianyu Zhang, David Lopez-Paz, Leon Bottou
CLeaR 2022 Simple Data Balancing Achieves Competitive Worst-Group-Accuracy Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, David Lopez-Paz
JMLR 2022 Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
NeurIPS 2021 An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers Ramakrishna Vedantam, David Lopez-Paz, David J Schwab
ICLR 2021 In Search of Lost Domain Generalization Ishaan Gulrajani, David Lopez-Paz
AAAI 2021 Using Hindsight to Anchor past Knowledge in Continual Learning Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz
ICLR 2020 Permutation Equivariant Models for Compositional Generalization in Language Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt
ICML 2019 First-Order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel, Yann Ollivier, Leon Bottou, Bernhard Schölkopf, David Lopez-Paz
IJCAI 2019 Interpolation Consistency Training for Semi-Supervised Learning Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz
NeurIPS 2019 Learning About an Exponential Amount of Conditional Distributions Mohamed Belghazi, Maxime Oquab, David Lopez-Paz
ICML 2019 Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio
NeurIPS 2019 Single-Model Uncertainties for Deep Learning Natasa Tagasovska, David Lopez-Paz
ICLR 2018 Mixup: Beyond Empirical Risk Minimization Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
CVPR 2017 Discovering Causal Signals in Images David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, Leon Bottou
NeurIPS 2017 Gradient Episodic Memory for Continual Learning David Lopez-Paz, Marc'Aurelio Ranzato
ICLR 2017 Revisiting Classifier Two-Sample Tests David Lopez-Paz, Maxime Oquab
AISTATS 2016 No Regret Bound for Extreme Bandits Robert Nishihara, David Lopez-Paz, Léon Bottou
JMLR 2016 Non-Linear Causal Inference Using Gaussianity Measures Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
ICLR 2016 Unifying Distillation and Privileged Information David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
JMLR 2015 The Randomized Causation Coefficient David Lopez-Paz, Krikamol Muandet, Benjamin Recht
ICML 2015 Towards a Learning Theory of Cause-Effect Inference David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Iliya Tolstikhin
ICML 2014 Randomized Nonlinear Component Analysis David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, Bernhard Schoelkopf
ICML 2013 Gaussian Process Vine Copulas for Multivariate Dependence David Lopez-Paz, Jose Miguel Hernández-Lobato, Ghahramani Zoubin
NeurIPS 2013 The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
NeurIPS 2012 Semi-Supervised Domain Adaptation with Non-Parametric Copulas David Lopez-paz, Jose M. Hernández-lobato, Bernhard Schölkopf