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Title
Cited by
Cited by
Year
Understanding AdamW through Proximal Methods and Scale-Freeness
Z Zhuang, M Liu, A Cutkosky, F Orabona
Transactions on Machine Learning Research, 2022
672022
Robustness to Unbounded Smoothness of Generalized SignSGD
M Crawshaw, M Liu, F Orabona, W Zhang, Z Zhuang
Advances in Neural Information Processing Systems 35, 9955--9968, 2022
582022
A second look at exponential and cosine step sizes: Simplicity, adaptivity, and performance
X Li, Z Zhuang, F Orabona
International Conference on Machine Learning, 6553-6564, 2021
35*2021
A communication-efficient distributed gradient clipping algorithm for training deep neural networks
M Liu, Z Zhuang, Y Lei, C Liao
Advances in Neural Information Processing Systems 35, 26204-26217, 2022
202022
No-regret non-convex online meta-learning
Z Zhuang, Y Wang, K Yu, S Lu
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
202020
Surrogate losses for online learning of stepsizes in stochastic non-convex optimization
Z Zhuang, A Cutkosky, F Orabona
Proceedings of the 36th International Conference on Machine Learning 97 …, 2019
62019
Online meta-learning on non-convex setting
Z Zhuang, K Yu, S Lu, L Glass, Y Wang
Workshop on Meta-Learning at NeurIPS 2019, 2019
52019
Understanding adamw through proximal methods and scale-freeness. arXiv 2022
Z Zhuang, M Liu, A Cutkosky, F Orabona
arXiv preprint arXiv:2202.00089, 0
5
Adaptive Strategies in Non-convex Optimization
Z Zhuang
Boston University, 2023
42023
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Articles 1–9