7 citations · 9 across the 5 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
stat.ML2021
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Xiaoxia Wu, Yuege Xie, Simon Du +1
We propose a computationally-friendly adaptive learning rate schedule, "AdaLoss", which directly uses the information of the loss function to adjust the stepsize in gradient descen…
stat.ML2019★ 7 cited
Linear Convergence of Adaptive Stochastic Gradient Descent
Yuege Xie, Xiaoxia Wu, Rachel Ward
We prove that the norm version of the adaptive stochastic gradient method (AdaGrad-Norm) achieves a linear convergence rate for a subset of either strongly convex functions or non-…