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MADA: Meta-Adaptive Optimizers through hyper-gradient Descent
v1v2v3 (latest)

MADA: Meta-Adaptive Optimizers through hyper-gradient Descent

International Conference on Machine Learning (ICML), 2024
17 January 2024
Kaan Ozkara
Can Karakus
Parameswaran Raman
Mingyi Hong
Shoham Sabach
Branislav Kveton
Volkan Cevher
ArXiv (abs)PDFHTMLGithub (56141★)

Papers citing "MADA: Meta-Adaptive Optimizers through hyper-gradient Descent"

3 / 3 papers shown
Gradient Methods with Online Scaling Part I. Theoretical Foundations
Gradient Methods with Online Scaling Part I. Theoretical Foundations
Wenzhi Gao
Ya-Chi Chu
Yinyu Ye
Madeleine Udell
362
4
0
29 May 2025
Stochastic Rounding for LLM Training: Theory and Practice
Stochastic Rounding for LLM Training: Theory and PracticeInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Kaan Ozkara
Tao Yu
Youngsuk Park
276
4
0
27 Feb 2025
MetaOptimize: A Framework for Optimizing Step Sizes and Other
  Meta-parameters
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Arsalan Sharifnassab
Saber Salehkaleybar
Richard Sutton
468
3
0
04 Feb 2024
1
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