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math.OC2023★ 1 cited
Two Sides of One Coin: the Limits of Untuned SGD and the Power of Adaptive Methods
Junchi Yang, Xiang Li, Ilyas Fatkhullin +1
The classical analysis of Stochastic Gradient Descent (SGD) with polynomially decaying stepsize relies on well-tuned depending on problem parameters such as…
math.OC2022
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
Xiang Li, Junchi Yang, Niao He
Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimiz…
math.OC2022
Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization
Junchi Yang, Xiang Li, Niao He
Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization owing to their parameter-agnostic ability -- requiring no a priori knowledge about problem-spe…