49 citations · 131 across the 37 of their papers we have counts for
4 papers · 1 filter
Fast model selection by limiting SVM training times
Aydin Demircioglu, Daniel Horn, Tobias Glasmachers +2
Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods. But for optimal predictive performance, time-consuming parameter tuning is cruc…
Coordinate Descent with Online Adaptation of Coordinate Frequencies
Tobias Glasmachers, Ürün Dogan
Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning. They are particularly popular for training li…
Accelerated Linear SVM Training with Adaptive Variable Selection Frequencies
Tobias Glasmachers, Ürün Dogan
Support vector machine (SVM) training is an active research area since the dawn of the method. In recent years there has been increasing interest in specialized solvers for the imp…
Natural Evolution Strategies
Daan Wierstra, Tom Schaul, Tobias Glasmachers +2
This paper presents Natural Evolution Strategies (NES), a recent family of algorithms that constitute a more principled approach to black-box optimization than established evolutio…