3 accepted papers
This paper presents and analyzes constant step size stochastic gradient algorithms in reproducing kernel Hilbert Space (RKHS), which encapsulates various adaptive nonlinear interpolation schemes. The hyperparameters of the function iterates are updated via a distribution that depends on the estimate…
Projection based algorithms are popular in the literature for online convex optimization with convex constraints and the projection step results in a bottleneck for the practical implementation of the algorithms. To avoid this bottleneck, we propose a projection-free scheme based on Frank-Wolfe: whe…
In this paper, we propose a communication-efficient decen-tralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct method of multiplier (GADMM), thereby ensuri…