14 citations · 23 across the 6 of their papers we have counts for
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math.OC2019
Last-iterate convergence rates for min-max optimization
Jacob Abernethy, Kevin A. Lai, Andre Wibisono
While classic work in convex-concave min-max optimization relies on average-iterate convergence results, the emergence of nonconvex applications such as training Generative Adversa…
math.OC2019
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions
Ashia Wilson, Lester Mackey, Andre Wibisono
We present a family of algorithms, called descent algorithms, for optimizing convex and non-convex functions. We also introduce a new first-order algorithm, called rescaled gradien…
math.OC2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Andre Wibisono
We study sampling as optimization in the space of measures. We focus on gradient flow-based optimization with the Langevin dynamics as a case study. We investigate the source of th…