Hongzhou Lin
Cited by
Cited by
A universal catalyst for first-order optimization
H Lin, J Mairal, Z Harchaoui
Advances in neural information processing systems 28, 2015
Resnet with one-neuron hidden layers is a universal approximator
H Lin, S Jegelka
Advances in neural information processing systems 31, 2018
Catalyst acceleration for first-order convex optimization: from theory to practice
H Lin, J Mairal, Z Harchaoui
Journal of Machine Learning Research 18 (1), 7854-7907, 2018
Catalyst for gradient-based nonconvex optimization
C Paquette, H Lin, D Drusvyatskiy, J Mairal, Z Harchaoui
International Conference on Artificial Intelligence and Statistics, 613-622, 2018
Complexity of finding stationary points of nonconvex nonsmooth functions
J Zhang, H Lin, S Jegelka, S Sra, A Jadbabaie
International Conference on Machine Learning, 11173-11182, 2020
An inexact variable metric proximal point algorithm for generic quasi-Newton acceleration
H Lin, J Mairal, Z Harchaoui
SIAM Journal on Optimization 29 (2), 1408-1443, 2019
Ideal: Inexact decentralized accelerated augmented lagrangian method
Y Arjevani, J Bruna, B Can, M Gürbüzbalaban, S Jegelka, H Lin
Advances in Neural Information Processing Systems 33, 20648--20659, 2020
Delayed gradient averaging: Tolerate the communication latency for federated learning
L Zhu, H Lin, Y Lu, Y Lin, S Han
Advances in Neural Information Processing Systems 34, 29995-30007, 2021
Stochastic Optimization with Non-stationary Noise
J Zhang, H Lin, S Das, S Sra, A Jadbabaie
arXiv preprint arXiv:2006.04429, 2020
On the complexity of minimizing convex finite sums without using the indices of the individual functions
Y Arjevani, A Daniely, S Jegelka, H Lin
arXiv preprint arXiv:2002.03273, 2020
Perceptual Regularization: Visualizing and Learning Generalizable Representations
H Lin, J Robinson, S Jegelka
Algorithmes d'accélération générique pour les méthodes d'optimisation en apprentissage statistique
H Lin
Université Grenoble Alpes (ComUE), 2017
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