170 citations · 909 across the 66 of their papers we have counts for
Showing 2019 · stat.MLShow all
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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-…
stat.ML2019
Bias of Homotopic Gradient Descent for the Hinge Loss
Denali Molitor, Deanna Needell, Rachel Ward
Gradient descent is a simple and widely used optimization method for machine learning. For homogeneous linear classifiers applied to separable data, gradient descent has been shown…
stat.ML2019★ 15 cited
AdaOja: Adaptive Learning Rates for Streaming PCA
Amelia Henriksen, Rachel Ward
Oja's algorithm has been the cornerstone of streaming methods in Principal Component Analysis (PCA) since it was first proposed in 1982. However, Oja's algorithm does not have a st…