| On Lazy Training in Differentiable Programming L Chizat, E Oyallon, F Bach Advances in Neural Information Processing Systems, 2937-2947, 2019 | 1253 | 2019 |
| On the global convergence of gradient descent for over-parameterized models using optimal transport L Chizat, F Bach Advances in Neural Information Processing Systems, 3036-3046, 2018 | 1052 | 2018 |
| Scaling algorithms for unbalanced optimal transport problems L Chizat, G Peyré, B Schmitzer, FX Vialard Mathematics of Computation 87 (314), 2563–2609, 2018 | 602* | 2018 |
| Implicit bias of gradient descent for wide two-layer neural networks trained with the logistic loss L Chizat, F Bach Proceedings of Thirty Third Conference on Learning Theory 125, 1305-1338, 2020 | 464 | 2020 |
| Unbalanced optimal transport: Dynamic and Kantorovich formulations L Chizat, G Peyré, B Schmitzer, FX Vialard Journal of Functional Analysis 274 (11), 3090-3123, 2018 | 434 | 2018 |
| Sample complexity of Sinkhorn divergences A Genevay, L Chizat, F Bach, M Cuturi, G Peyré International Conference on Artificial Intelligence and Statistics, 1574-1583, 2019 | 428 | 2019 |
| An interpolating distance between optimal transport and Fisher–Rao metrics L Chizat, G Peyré, B Schmitzer, FX Vialard Foundations of Computational Mathematics 18 (1), 1-44, 2018 | 316 | 2018 |
| Faster Wasserstein distance estimation with the Sinkhorn divergence L Chizat, P Roussillon, F Léger, FX Vialard, G Peyré Advances in neural information processing systems 33, 2257-2269, 2020 | 244 | 2020 |
| Sparse optimization on measures with over-parameterized gradient descent L Chizat Mathematical Programming, 1-46, 2021 | 143 | 2021 |
| Statistical and topological properties of sliced probability divergences K Nadjahi, A Durmus, L Chizat, S Kolouri, S Shahrampour, U Simsekli Advances in Neural Information Processing Systems 33, 20802-20812, 2020 | 129 | 2020 |
| Mean-field Langevin dynamics: exponential convergence and annealing L Chizat Transaction on Machine Learning Research, 2022 | 124 | 2022 |
| Quantum entropic regularization of matrix-valued optimal transport G Peyré, L Chizat, FX Vialard, J Solomon European Journal of Applied Mathematics 30 (6), 1079-1102, 2019 | 61* | 2019 |
| Overrelaxed Sinkhorn–Knopp algorithm for regularized optimal transport A Thibault, L Chizat, C Dossal, N Papadakis Algorithms 14 (5), 143, 2021 | 56* | 2021 |
| Unbalanced Optimal Transport: Models, Numerical Methods, Applications L Chizat Université Paris Dauphine - PSL, 2017 | 52 | 2017 |
| Trajectory Inference via Mean-field Langevin in Path Space L Chizat, S Zhang, M Heitz, G Schiebinger arXiv preprint arXiv:2205.07146, 2022 | 49 | 2022 |
| Gradient descent on infinitely wide neural networks: Global convergence and generalization F Bach, L Chizat arXiv preprint arXiv:2110.08084, 2021 | 38 | 2021 |
| Infinite‐width limit of deep linear neural networks L Chizat, M Colombo, X Fernández‐Real, A Figalli Communications on Pure and Applied Mathematics 77 (10), 3958-4007, 2024 | 35 | 2024 |
| Convergence rates of gradient methods for convex optimization in the space of measures L Chizat Open Journal of Mathematical Optimization 3, 1-19, 2022 | 34 | 2022 |
| A tumor growth model of Hele-Shaw type as a gradient flow S Di Marino, L Chizat ESAIM: Control, Optimisation and Calculus of Variations 26, 103, 2020 | 31* | 2020 |
| Displacement smoothness of entropic optimal transport G Carlier, L Chizat, M Laborde ESAIM: Control, Optimisation and Calculus of Variations 30, 25, 2024 | 30* | 2024 |
