Federated learning (FL) approaches for saddle point problems (SPP) have
...
We propose a study of structured non-convex non-concave min-max problems...
We study the sample complexity of reducing reinforcement learning to a
s...
In this work, we present new simple and optimal algorithms for solving t...
We propose and analyze a stochastic Newton algorithm for homogeneous
dis...
We provide several algorithms for constrained optimization of a large cl...
We give almost-linear-time algorithms for constructing sparsifiers with ...
We resolve the min-max complexity of distributed stochastic convex
optim...
We provide improved convergence rates for constrained convex-concave min...
We study local SGD (also known as parallel SGD and federated averaging),...
We provide improved convergence rates for various non-smooth
optimizatio...
We study the control of a linear dynamical system with adversarial
distu...
Adaptive regularization methods come in diagonal and full-matrix variant...
We propose a principled method for kernel learning, which relies on a
Fo...
We design a non-convex second-order optimization algorithm that is guara...
First-order stochastic methods are the state-of-the-art in large-scale
m...