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Nidham Gazagnadou
Nidham Gazagnadou
Sony AI
在 sony.com 的电子邮件经过验证 - 首页
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引用次数
引用次数
年份
Optimal mini-batch and step sizes for saga
N Gazagnadou, R Gower, J Salmon
International conference on machine learning, 2142-2150, 2019
422019
Towards closing the gap between the theory and practice of SVRG
O Sebbouh, N Gazagnadou, S Jelassi, F Bach, R Gower
Advances in neural information processing systems 32, 2019
202019
Cutting some slack for SGD with adaptive Polyak stepsizes
RM Gower, M Blondel, N Gazagnadou, F Pedregosa
arXiv preprint arXiv:2202.12328, 2022
192022
RidgeSketch: A Fast sketching based solver for large scale ridge regression
N Gazagnadou, M Ibrahim, RM Gower
SIAM Journal on Matrix Analysis and Applications 43 (3), 1440-1468, 2022
62022
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
K Yi, N Gazagnadou, P Richtárik, L Lyu
arXiv preprint arXiv:2404.09816, 2024
22024
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?
X Sun, N Gazagnadou, V Sharma, L Lyu, H Li, L Zheng
Conference on Neural Information Processing Systems 2023, 2023
22023
On the hardness of robustness transfer: A perspective from Rademacher complexity over symmetric difference hypothesis space
Y Deng, N Gazagnadou, J Hong, M Mahdavi, L Lyu
arXiv preprint arXiv:2302.12351, 2023
12023
Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems
N Gazagnadou
Institut Polytechnique de Paris, 2021
2021
Exercise List: Proving convergence of the Stochastic Gradient Descent and Coordinate Descent on the Ridge Regression Problem.
RM Gower, F Bach, N Gazagnadou
2019
Personalization Mitigates the Perils of Local SGD for Heterogeneous Distributed Learning
KK Patel, N Gazagnadou, L Wang, L Lyu
Rademacher Complexity Over Class for Adversarially Robust Domain Adaptation
Y Deng, N Gazagnadou, J Hong, M Mahdavi, L Lyu
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