| mixup: Beyond empirical risk minimization H Zhang, M Cisse, YN Dauphin, D Lopez-Paz ICLR, 2018 | 6648 | 2018 |
| Gradient Episodic Memory for Continual Learning D Lopez-Paz, MA Ranzato NeurIPS, 2017 | 1775 | 2017 |
| Invariant risk minimization M Arjovsky, L Bottou, I Gulrajani, D Lopez-Paz arXiv, 2019 | 1231 | 2019 |
| Manifold mixup: learning better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ... ICML, 2019 | 883* | 2019 |
| In Search of Lost Domain Generalization I Gulrajani, D Lopez-Paz ICLR, 2021 | 606 | 2021 |
| Interpolation consistency training for semi-supervised learning V Verma, A Lamb, J Kannala, Y Bengio, D Lopez-Paz IJCAI, 2019 | 525 | 2019 |
| Unifying distillation and privileged information D Lopez-Paz, L Bottou, B Schölkopf, V Vapnik ICLR, 2016 | 432 | 2016 |
| Optimizing the latent space of generative networks P Bojanowski, A Joulin, D Lopez-Paz, A Szlam ICML, 2018 | 393 | 2018 |
| Revisiting classifier two-sample tests D Lopez-Paz, M Oquab ICLR, 2017 | 319 | 2017 |
| The Randomized Dependence Coefficient D Lopez-Paz, P Hennig, B Schölkopf NeurIPS, 2013 | 221 | 2013 |
| Single-Model Uncertainties for Deep Learning N Tagasovska, D Lopez-Paz NeurIPS, 2019 | 215* | 2019 |
| Randomized Nonlinear Component Analysis D Lopez-Paz, S Sra, A Smola, Z Ghahramani, B Schölkopf ICML, 2014 | 208 | 2014 |
| Discovering causal signals in images D Lopez-Paz, R Nishihara, S Chintala, B Scholkopf, L Bottou CVPR, 2017 | 192 | 2017 |
| Towards a Learning Theory of Cause-Effect Inference D Lopez-Paz, K Muandet, B Schölkopf, I Tolstikhin ICML, 2015 | 178 | 2015 |
| Using hindsight to anchor past knowledge in continual learning A Chaudhry, A Gordo, PK Dokania, P Torr, D Lopez-Paz AAAI, 2021 | 134 | 2021 |
| First-order adversarial vulnerability of neural networks and input dimension CJ Simon-Gabriel, Y Ollivier, L Bottou, B Schölkopf, D Lopez-Paz ICML, 2019 | 126 | 2019 |
| Learning functional causal models with generative neural networks O Goudet, D Kalainathan, P Caillou, I Guyon, D Lopez-Paz, M Sebag Explainable and Interpretable Models in Computer Vision and Machine Learning …, 2018 | 123 | 2018 |
| Permutation equivariant models for compositional generalization in language J Gordon, D Lopez-Paz, M Baroni, D Bouchacourt ICLR, 2020 | 74 | 2020 |
| SAM: Structural Agnostic Model, causal discovery and penalized adversarial learning D Kalainathan, O Goudet, I Guyon, D Lopez-Paz, M Sebag arXiv, 2018 | 71* | 2018 |
| Poincaré maps for analyzing complex hierarchies in single-cell data A Klimovskaia, D Lopez-Paz, L Bottou, M Nickel Nature Communications, 2020 | 62 | 2020 |
