David Lopez-Paz

78 papers A* 22A 1Journal 45Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ReasonCACHE: Teaching LLMs To Reason Without Weight Updates. CoRR Sharut Gupta, Phillip Isola, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja, Mark Ibrahim, Mohammad Pezeshki
2025	J	`jnl`	A Differentiable Rank-Based Objective For Better Feature Learning. CoRR Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, Levent Sagun
2025	J	`jnl`	Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries. CoRR Divyat Mahajan, Sachin Goyal, Badr Youbi Idrissi, Mohammad Pezeshki, Ioannis Mitliagkas, David Lopez-Paz, Kartik Ahuja
2025	J	`jnl`	Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling. CoRR Sachin Goyal, David Lopez-Paz, Kartik Ahuja
2025	J	`jnl`	From Bytes to Ideas: Language Modeling with Autoregressive U-Nets. CoRR Mathurin Videau, Badr Youbi Idrissi, Alessandro Ferreira Leite, Marc Schoenauer, Olivier Teytaud, David Lopez-Paz
2025	J	`jnl`	Set Block Decoding is a Language Model Inference Accelerator. CoRR Itai Gat, Heli Ben-Hamu, Marton Havasi, Daniel Haziza, Jeremy Reizenstein, Gabriel Synnaeve, David Lopez-Paz, Brian Karrer, Yaron Lipman
2025	A*	`conf`	The Pitfalls of Memorization: When Memorization Hurts Generalization. ICLR Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, Pascal Vincent
2025	J	`jnl`	Unveiling Simplicities of Attention: Adaptive Long-Context Head Identification. CoRR Konstantin Donhauser, Charles Arnal, Mohammad Pezeshki, Vivien Cabannes, David Lopez-Paz, Kartik Ahuja
2024	A*	`conf`	Better & Faster Large Language Models via Multi-token Prediction. ICML Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve
2024	J	`jnl`	Better & Faster Large Language Models via Multi-token Prediction. CoRR Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve
2024	A*	`conf`	Context is Environment. ICLR Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
2024	A*	`conf`	Discovering Environments with XRM. ICML Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
2024	J	`jnl`	Flow Matching Guide and Code. CoRR Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky T. Q. Chen, David Lopez-Paz, Heli Ben-Hamu, Itai Gat
2024	J	`jnl`	The Pitfalls of Memorization: When Memorization Hurts Generalization. CoRR Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, Pascal Vincent
2023	J	`jnl`	A Closer Look at In-Context Learning under Distribution Shifts. CoRR Kartik Ahuja, David Lopez-Paz
2023	J	`jnl`	Context is Environment. CoRR Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja
2023	J	`jnl`	Discovering environments with XRM. CoRR Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
2023	A*	`conf`	ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. ICLR Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim
2023	A*	`conf`	Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization. ICML Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz
2023	J	`jnl`	Unified Uncertainty Calibration. CoRR Kamalika Chaudhuri, David Lopez-Paz
2023	A*	`conf`	Why does Throwing Away Data Improve Worst-Group Error? ICML Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz
2022	J	`jnl`	ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. CoRR Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim
2022	J	`jnl`	Interpolation consistency training for semi-supervised learning. Neural Networks Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz
2022	J	`jnl`	Measuring and signing fairness as performance under multiple stakeholder distributions. CoRR David Lopez-Paz, Diane Bouchacourt, Levent Sagun, Nicolas Usunier
2022	J	`jnl`	Recycling diverse models for out-of-distribution generalization. CoRR Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz
2022	A*	`conf`	Rich Feature Construction for the Optimization-Generalization Dilemma. ICML Jianyu Zhang, David Lopez-Paz, Léon Bottou
2022	J	`jnl`	Rich Feature Construction for the Optimization-Generalization Dilemma. CoRR Jianyu Zhang, David Lopez-Paz, Léon Bottou
2022	—	`conf`	Simple data balancing achieves competitive worst-group-accuracy. CLeaR Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
2022	J	`jnl`	Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. J. Mach. Learn. Res. Diviyan Kalainathan, Olivier Goudet, Isabelle Guyon, David Lopez-Paz, Michèle Sebag
2022	J	`jnl`	Throwing Away Data Improves Worst-Class Error in Imbalanced Classification. CoRR Martín Arjovsky, Kamalika Chaudhuri, David Lopez-Paz
2021	A*	`conf`	An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. NeurIPS Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab
2021	A*	`conf`	In Search of Lost Domain Generalization. ICLR Ishaan Gulrajani, David Lopez-Paz
2021	J	`jnl`	Linear unit-tests for invariance discovery. CoRR Benjamin Aubin, Agnieszka Slowik, Martín Arjovsky, Léon Bottou, David Lopez-Paz
2021	J	`jnl`	Simple data balancing achieves competitive worst-group-accuracy. CoRR Badr Youbi Idrissi, Martín Arjovsky, Mohammad Pezeshki, David Lopez-Paz
2021	A*	`conf`	Using Hindsight to Anchor Past Knowledge in Continual Learning. AAAI Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz
2021	J	`jnl`	What classifiers know what they don't? CoRR Mohamed Ishmael Belghazi, David Lopez-Paz
2020	J	`jnl`	Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations. NeuroImage Jean-Rémi King, François Charton, David Lopez-Paz, Maxime Oquab
2020	J	`jnl`	In Search of Lost Domain Generalization. CoRR Ishaan Gulrajani, David Lopez-Paz
2020	A*	`conf`	Permutation Equivariant Models for Compositional Generalization in Language. ICLR Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt
2020	J	`jnl`	Using Hindsight to Anchor Past Knowledge in Continual Learning. CoRR Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz
2019	A*	`conf`	First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz
2019	J	`jnl`	Interpolation Consistency Training for Semi-Supervised Learning. CoRR Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz
2019	A*	`conf`	Interpolation Consistency Training for Semi-supervised Learning. IJCAI Vikas Verma, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz
2019	J	`jnl`	Invariant Risk Minimization. CoRR Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz
2019	A*	`conf`	Learning about an exponential amount of conditional distributions. NeurIPS Mohamed Ishmael Belghazi, Maxime Oquab, David Lopez-Paz
2019	J	`jnl`	Learning about an exponential amount of conditional distributions. CoRR Mohamed Ishmael Belghazi, Maxime Oquab, Yann LeCun, David Lopez-Paz
2019	A*	`conf`	Manifold Mixup: Better Representations by Interpolating Hidden States. ICML Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio
2019	—	`ch.`	Non-linear Causal Inference Using Gaussianity Measures. Cause Effect Pairs in Machine Learning Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
2019	A*	`conf`	Single-Model Uncertainties for Deep Learning. NeurIPS Natasa Tagasovska, David Lopez-Paz
2018	J	`jnl`	Adversarial Vulnerability of Neural Networks Increases With Input Dimension. CoRR Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz
2018	—	`conf`	Causal Discovery Using Proxy Variables. ICLR (Workshop) Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
2018	—	`conf`	Easing non-convex optimization with neural networks. ICLR (Workshop) David Lopez-Paz, Levent Sagun
2018	J	`jnl`	Frequentist uncertainty estimates for deep learning. CoRR Natasa Tagasovska, David Lopez-Paz
2018	A*	`conf`	Optimizing the Latent Space of Generative Networks. ICML Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
2018	—	`conf`	mixup: Beyond Empirical Risk Minimization. ICLR (Poster) Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
2017	J	`jnl`	Causal Discovery Using Proxy Variables. CoRR Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
2017	A*	`conf`	Discovering Causal Signals in Images. CVPR David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
2017	—	`conf`	Geometrical Insights for Implicit Generative Modeling. Braverman Readings in Machine Learning Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
2017	J	`jnl`	Geometrical Insights for Implicit Generative Modeling. CoRR Léon Bottou, Martín Arjovsky, David Lopez-Paz, Maxime Oquab
2017	—	`conf`	Gradient Episodic Memory for Continual Learning. NIPS David Lopez-Paz, Marc'Aurelio Ranzato
2017	J	`jnl`	Gradient Episodic Memory for Continuum Learning. CoRR David Lopez-Paz, Marc'Aurelio Ranzato
2017	J	`jnl`	Optimizing the Latent Space of Generative Networks. CoRR Piotr Bojanowski, Armand Joulin, David Lopez-Paz, Arthur Szlam
2017	—	`conf`	Patient-Driven Privacy Control through Generalized Distillation. PAC Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel
2017	—	`conf`	Revisiting Classifier Two-Sample Tests. ICLR (Poster) David Lopez-Paz, Maxime Oquab
2017	J	`jnl`	mixup: Beyond Empirical Risk Minimization. CoRR Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David Lopez-Paz
2016	J	`jnl`	Discovering Causal Signals in Images. CoRR David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Schölkopf, Léon Bottou
2016	J	`jnl`	Minimax Lower Bounds for Realizable Transductive Classification. CoRR Ilya O. Tolstikhin, David Lopez-Paz
2016	A	`conf`	No Regret Bound for Extreme Bandits. AISTATS Robert Nishihara, David Lopez-Paz, Léon Bottou
2016	J	`jnl`	Non-linear Causal Inference using Gaussianity Measures. J. Mach. Learn. Res. Daniel Hernández-Lobato, Pablo Morales-Mombiela, David Lopez-Paz, Alberto Suárez
2016	J	`jnl`	Patient-Driven Privacy Control through Generalized Distillation. CoRR Z. Berkay Celik, David Lopez-Paz, Patrick D. McDaniel
2016	A*	`conf`	Unifying distillation and privileged information. ICLR (Poster) David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
2015	J	`jnl`	No Regret Bound for Extreme Bandits. CoRR Robert Nishihara, David Lopez-Paz, Léon Bottou
2015	J	`jnl`	The Randomized Causation Coefficient. J. Mach. Learn. Res. David Lopez-Paz, Krikamol Muandet, Benjamin Recht
2015	A*	`conf`	Towards a Learning Theory of Cause-Effect Inference. ICML David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
2014	A*	`conf`	Randomized Nonlinear Component Analysis. ICML David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
2014	J	`jnl`	Randomized Nonlinear Component Analysis. CoRR David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf
2013	—	`conf`	Gaussian Process Vine Copulas for Multivariate Dependence. ICML (2) David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani
2013	J	`jnl`	Semi-Supervised Domain Adaptation with Non-Parametric Copulas CoRR David Lopez-Paz, José Miguel Hernández-Lobato, Bernhard Schölkopf