no code implementations • 22 Feb 2024 • Massil Hihat, Guillaume Garrigos, Adeline Fermanian, Simon Bussy
In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate.
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.
no code implementations • NeurIPS 2023 • Justin Domke, Guillaume Garrigos, Robert Gower
Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds.
no code implementations • 28 Mar 2017 • Guillaume Garrigos, Lorenzo Rosasco, Silvia Villa
We provide a comprehensive study of the convergence of the forward-backward algorithm under suitable geometric conditions, such as conditioning or {\L}ojasiewicz properties.