no code implementations • 20 Feb 2024 • Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert Gower
We then derive iterative methods based on the stochastic proximal point method for computing the geometric median and generalizations thereof.
no code implementations • 26 Jul 2023 • Guillaume Garrigos, Robert M. Gower, Fabian Schaipp
We then move onto to develop $\texttt{FUVAL}$, a variant of $\texttt{SPS}_+$ where the loss values at optimality are gradually learned, as opposed to being given.
1 code implementation • 12 May 2023 • Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
MoMo uses momentum estimates of the batch losses and gradients sampled at each iteration to build a model of the loss function.
1 code implementation • 12 Jan 2023 • Fabian Schaipp, Robert M. Gower, Michael Ulbrich
Developing a proximal variant of SPS is particularly important, since SPS requires a lower bound of the objective function to work well.
1 code implementation • 1 Apr 2022 • Andre Milzarek, Fabian Schaipp, Michael Ulbrich
We develop an implementable stochastic proximal point (SPP) method for a class of weakly convex, composite optimization problems.