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Found 6 papers, 2 papers with code
Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function
We develop a novel and single-loop variance-reduced algorithm to solve a class of stochastic nonconvex-convex minimax problems involving a nonconvex-linear objective function, which has various applications in different fields such as ma- chine learning and robust optimization.
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
In this note we propose a new variant of the hybrid variance-reduced proximal gradient method in [7] to solve a common stochastic composite nonconvex optimization problem under standard assumptions.
Robust and Generalizable Visual Representation Learning via Random Convolutions
In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation.
Ranked #110 on
Domain Generalization
on PACS
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
This problem class has several computational challenges due to its nonsmoothness, nonconvexity, nonlinearity, and non-separability of the objective functions.
A New Randomized Primal-Dual Algorithm for Convex Optimization with Optimal Last Iterate Rates
We develop a novel unified randomized block-coordinate primal-dual algorithm to solve a class of nonsmooth constrained convex optimization problems, which covers different existing variants and model settings from the literature.
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization
We demonstrate how to scalably solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO) over the constraint set.
