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2020 – today
- 2024
[c34]Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja:
Context is Environment. ICLR 2024- [c33]Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve:
Better & Faster Large Language Models via Multi-token Prediction. ICML 2024 - [c32]Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz:
Discovering Environments with XRM. ICML 2024 - [i31]Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve:
Better & Faster Large Language Models via Multi-token Prediction. CoRR abs/2404.19737 (2024) - 2023
- [c31]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 - [c30]Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz:
Why does Throwing Away Data Improve Worst-Group Error? ICML 2023: 4144-4188 - [c29]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: 28656-28679 - [i30]Kartik Ahuja, David Lopez-Paz:
A Closer Look at In-Context Learning under Distribution Shifts. CoRR abs/2305.16704 (2023) - [i29]Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja:
Context is Environment. CoRR abs/2309.09888 (2023) - [i28]Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz:
Discovering environments with XRM. CoRR abs/2309.16748 (2023) - [i27]Kamalika Chaudhuri, David Lopez-Paz:
Unified Uncertainty Calibration. CoRR abs/2310.01202 (2023) - 2022
- [j5]Diviyan Kalainathan, Olivier Goudet
, Isabelle Guyon, David Lopez-Paz, Michèle Sebag:
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. J. Mach. Learn. Res. 23: 219:1-219:62 (2022) - [j4]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: 90-106 (2022) - [c28]Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz:
Simple data balancing achieves competitive worst-group-accuracy. CLeaR 2022: 336-351 - [c27]Jianyu Zhang, David Lopez-Paz, Léon Bottou:
Rich Feature Construction for the Optimization-Generalization Dilemma. ICML 2022: 26397-26411 - [i26]Jianyu Zhang, David Lopez-Paz, Léon Bottou:
Rich Feature Construction for the Optimization-Generalization Dilemma. CoRR abs/2203.15516 (2022) - [i25]Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz:
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR abs/2205.11672 (2022) - [i24]David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier:
Measuring and signing fairness as performance under multiple stakeholder distributions. CoRR abs/2207.09960 (2022) - [i23]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 abs/2211.01866 (2022) - [i22]Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz:
Recycling diverse models for out-of-distribution generalization. CoRR abs/2212.10445 (2022) - 2021
- [c26]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: 6993-7001 - [c25]Ishaan Gulrajani, David Lopez-Paz:
In Search of Lost Domain Generalization. ICLR 2021 - [c24]Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab:
An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. NeurIPS 2021: 28131-28143 - [i21]Benjamin Aubin, Agnieszka Slowik, Martín Arjovsky, Léon Bottou, David Lopez-Paz:
Linear unit-tests for invariance discovery. CoRR abs/2102.10867 (2021) - [i20]Mohamed Ishmael Belghazi, David Lopez-Paz:
What classifiers know what they don't? CoRR abs/2107.06217 (2021) - [i19]Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz:
Simple data balancing achieves competitive worst-group-accuracy. CoRR abs/2110.14503 (2021) - 2020
- [j3]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: 117028 (2020) - [c23]Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt:
Permutation Equivariant Models for Compositional Generalization in Language. ICLR 2020 - [i18]Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz:
Using Hindsight to Anchor Past Knowledge in Continual Learning. CoRR abs/2002.08165 (2020) - [i17]Ishaan Gulrajani, David Lopez-Paz:
In Search of Lost Domain Generalization. CoRR abs/2007.01434 (2020)
2010 – 2019
- 2019
- [c22]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: 5809-5817 - [c21]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: 6438-6447 - [c20]Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz:
Interpolation Consistency Training for Semi-supervised Learning. IJCAI 2019: 3635-3641 - [c19]Natasa Tagasovska, David Lopez-Paz:
Single-Model Uncertainties for Deep Learning. NeurIPS 2019: 6414-6425 - [c18]Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz:
Learning about an exponential amount of conditional distributions. NeurIPS 2019: 13703-13714 - [p1]Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference Using Gaussianity Measures. Cause Effect Pairs in Machine Learning 2019: 257-299 - [i16]Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz:
Learning about an exponential amount of conditional distributions. CoRR abs/1902.08401 (2019) - [i15]Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz:
Interpolation Consistency Training for Semi-Supervised Learning. CoRR abs/1903.03825 (2019) - [i14]Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz:
Invariant Risk Minimization. CoRR abs/1907.02893 (2019) - 2018
- [c17]David Lopez-Paz, Levent Sagun:
Easing non-convex optimization with neural networks. ICLR (Workshop) 2018 - [c16]Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz:
Causal Discovery Using Proxy Variables. ICLR (Workshop) 2018 - [c15]Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz:
mixup: Beyond Empirical Risk Minimization. ICLR (Poster) 2018 - [c14]Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam:
Optimizing the Latent Space of Generative Networks. ICML 2018: 599-608 - [i13]Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz:
Adversarial Vulnerability of Neural Networks Increases With Input Dimension. CoRR abs/1802.01421 (2018) - [i12]Natasa Tagasovska, David Lopez-Paz:
Frequentist uncertainty estimates for deep learning. CoRR abs/1811.00908 (2018) - 2017
- [c13]Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab:
Geometrical Insights for Implicit Generative Modeling. Braverman Readings in Machine Learning 2017: 229-268 - [c12]David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou:
Discovering Causal Signals in Images. CVPR 2017: 58-66 - [c11]David Lopez-Paz, Maxime Oquab:
Revisiting Classifier Two-Sample Tests. ICLR (Poster) 2017 - [c10]David Lopez-Paz, Marc'Aurelio Ranzato:
Gradient Episodic Memory for Continual Learning. NIPS 2017: 6467-6476 - [c9]Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel:
Patient-Driven Privacy Control through Generalized Distillation. PAC 2017: 1-12 - [i11]Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz:
Causal Discovery Using Proxy Variables. CoRR abs/1702.07306 (2017) - [i10]David Lopez-Paz, Marc'Aurelio Ranzato:
Gradient Episodic Memory for Continuum Learning. CoRR abs/1706.08840 (2017) - [i9]Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam:
Optimizing the Latent Space of Generative Networks. CoRR abs/1707.05776 (2017) - [i8]Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz:
mixup: Beyond Empirical Risk Minimization. CoRR abs/1710.09412 (2017) - [i7]Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab:
Geometrical Insights for Implicit Generative Modeling. CoRR abs/1712.07822 (2017) - 2016
- [j2]Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez:
Non-linear Causal Inference using Gaussianity Measures. J. Mach. Learn. Res. 17: 28:1-28:39 (2016) - [c8]Robert Nishihara, David Lopez-Paz, Léon Bottou:
No Regret Bound for Extreme Bandits. AISTATS 2016: 259-267 - [c7]David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik:
Unifying distillation and privileged information. ICLR (Poster) 2016 - [i6]Ilya O. Tolstikhin, David Lopez-Paz:
Minimax Lower Bounds for Realizable Transductive Classification. CoRR abs/1602.03027 (2016) - [i5]David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou:
Discovering Causal Signals in Images. CoRR abs/1605.08179 (2016) - [i4]Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel:
Patient-Driven Privacy Control through Generalized Distillation. CoRR abs/1611.08648 (2016) - 2015
- [j1]David Lopez-Paz, Krikamol Muandet, Benjamin Recht:
The Randomized Causation Coefficient. J. Mach. Learn. Res. 16: 2901-2907 (2015) - [c6]David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin:
Towards a Learning Theory of Cause-Effect Inference. ICML 2015: 1452-1461 - [i3]Robert Nishihara, David Lopez-Paz, Léon Bottou:
No Regret Bound for Extreme Bandits. CoRR abs/1508.02933 (2015) - 2014
- [c5]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. ICML 2014: 1359-1367 - [i2]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. CoRR abs/1402.0119 (2014) - 2013
- [c4]David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani:
Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) 2013: 10-18 - [c3]David López-Paz, Philipp Hennig, Bernhard Schölkopf:
The Randomized Dependence Coefficient. NIPS 2013: 1-9 - [i1]David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. CoRR abs/1301.0142 (2013) - 2012
- [c2]David López-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf:
Semi-Supervised Domain Adaptation with Non-Parametric Copulas. NIPS 2012: 674-682 - 2011
- [c1]Angela Fabregues, David López-Paz, Carles Sierra:
DipTools: experimental data visualization tool for the DipGame testbed. AAMAS 2011: 1315-1316
Coauthor Index
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