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Found 5 papers, 0 papers with code
Multivariate Online Linear Regression for Hierarchical Forecasting
In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate.
SGD with Clipping is Secretly Estimating the Median Gradient
We then derive iterative methods based on the stochastic proximal point method for computing the geometric median and generalizations thereof.
Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM
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.
Provable convergence guarantees for black-box variational inference
Black-box variational inference is widely used in situations where there is no proof that its stochastic optimization succeeds.
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry
We provide a comprehensive study of the convergence of the forward-backward algorithm under suitable geometric conditions, such as conditioning or {\L}ojasiewicz properties.
