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Lénaïc Chizat
2020 – today
- 2024
- [j8]Sebastian Neumayer, Lénaïc Chizat, Michael Unser:
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks. J. Mach. Learn. Res. 25: 15:1-15:24 (2024) - [j7]Karl Hajjar, Lénaïc Chizat, Christophe Giraud:
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit. J. Mach. Learn. Res. 25: 196:1-196:130 (2024) - [c10]Guillaume Wang, Lénaïc Chizat:
Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games. COLT 2024: 5345-5350 - [i19]Pierre Marion, Lénaïc Chizat:
Deep linear networks for regression are implicitly regularized towards flat minima. CoRR abs/2405.13456 (2024) - [i18]Lénaïc Chizat:
Annealed Sinkhorn for Optimal Transport: convergence, regularization path and debiasing. CoRR abs/2408.11620 (2024) - 2023
- [c9]Tomas Vaskevicius, Lénaïc Chizat:
Computational Guarantees for Doubly Entropic Wasserstein Barycenters. NeurIPS 2023 - [c8]Guillaume Wang, Lénaïc Chizat:
Local Convergence of Gradient Methods for Min-Max Games: Partial Curvature Generically Suffices. NeurIPS 2023 - [i17]Lénaïc Chizat:
Doubly Regularized Entropic Wasserstein Barycenters. CoRR abs/2303.11844 (2023) - [i16]Sebastian Neumayer, Lénaïc Chizat, Michael Unser:
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks. CoRR abs/2303.17805 (2023) - [i15]Guillaume Wang, Lénaïc Chizat:
Local Convergence of Gradient Methods for Min-Max Games under Partial Curvature. CoRR abs/2305.17275 (2023) - [i14]Lénaïc Chizat, Tomas Vaskevicius:
Computational Guarantees for Doubly Entropic Wasserstein Barycenters via Damped Sinkhorn Iterations. CoRR abs/2307.13370 (2023) - [i13]Lénaïc Chizat, Praneeth Netrapalli:
Steering Deep Feature Learning with Backward Aligned Feature Updates. CoRR abs/2311.18718 (2023) - 2022
- [j6]Lénaïc Chizat:
Sparse optimization on measures with over-parameterized gradient descent. Math. Program. 194(1): 487-532 (2022) - [j5]Lénaïc Chizat:
Convergence Rates of Gradient Methods for Convex Optimization in the Space of Measures. Open J. Math. Optim. 3: 1-19 (2022) - [j4]Lénaïc Chizat:
Mean-Field Langevin Dynamics : Exponential Convergence and Annealing. Trans. Mach. Learn. Res. 2022 (2022) - [c7]Lénaïc Chizat, Stephen Zhang, Matthieu Heitz, Geoffrey Schiebinger:
Trajectory Inference via Mean-field Langevin in Path Space. NeurIPS 2022 - [i12]Lénaïc Chizat, Stephen Zhang, Matthieu Heitz, Geoffrey Schiebinger:
Trajectory Inference via Mean-field Langevin in Path Space. CoRR abs/2205.07146 (2022) - [i11]Guillaume Wang, Lénaïc Chizat:
An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games. CoRR abs/2211.01280 (2022) - [i10]Karl Hajjar, Lénaïc Chizat:
Symmetries in the dynamics of wide two-layer neural networks. CoRR abs/2211.08771 (2022) - [i9]Lénaïc Chizat, Maria Colombo, Xavier Fernández-Real, Alessio Figalli:
Infinite-width limit of deep linear neural networks. CoRR abs/2211.16980 (2022) - 2021
- [j3]Alexis Thibault, Lénaïc Chizat, Charles Dossal, Nicolas Papadakis:
Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport. Algorithms 14(5): 143 (2021) - [i8]Francis R. Bach, Lenaïc Chizat:
Gradient Descent on Infinitely Wide Neural Networks: Global Convergence and Generalization. CoRR abs/2110.08084 (2021) - [i7]Karl Hajjar, Lénaïc Chizat, Christophe Giraud:
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit. CoRR abs/2110.15596 (2021) - 2020
- [c6]Lénaïc Chizat, Francis R. Bach:
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss. COLT 2020: 1305-1338 - [c5]Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré:
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence. NeurIPS 2020 - [c4]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS 2020 - [i6]Lénaïc Chizat, Francis R. Bach:
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss. CoRR abs/2002.04486 (2020) - [i5]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. CoRR abs/2003.05783 (2020)
2010 – 2019
- 2019
- [c3]Aude Genevay, Lénaïc Chizat, Francis R. Bach, Marco Cuturi, Gabriel Peyré:
Sample Complexity of Sinkhorn Divergences. AISTATS 2019: 1574-1583 - [c2]Lénaïc Chizat, Edouard Oyallon, Francis R. Bach:
On Lazy Training in Differentiable Programming. NeurIPS 2019: 2933-2943 - [i4]Jean-Luc Peyrot, Laurent Duval, Frédéric Payan, Lauriane Bouard, Lénaïc Chizat, Sébastien Schneider, Marc Antonini:
HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models. CoRR abs/1903.07614 (2019) - 2018
- [j2]Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard:
An Interpolating Distance Between Optimal Transport and Fisher-Rao Metrics. Found. Comput. Math. 18(1): 1-44 (2018) - [j1]Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, François-Xavier Vialard:
Scaling algorithms for unbalanced optimal transport problems. Math. Comput. 87(314): 2563-2609 (2018) - [c1]Lénaïc Chizat, Francis R. Bach:
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. NeurIPS 2018: 3040-3050 - [i3]Lenaïc Chizat, Francis R. Bach:
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport. CoRR abs/1805.09545 (2018) - [i2]Lénaïc Chizat, Francis R. Bach:
A Note on Lazy Training in Supervised Differentiable Programming. CoRR abs/1812.07956 (2018) - 2016
- [i1]Gabriel Peyré, Lenaïc Chizat, François-Xavier Vialard, Justin Solomon:
Quantum Optimal Transport for Tensor Field Processing. CoRR abs/1612.08731 (2016)
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last updated on 2024-10-07 21:19 CEST by the dblp team
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