Projection-free Online Learning in Dynamic Environments

Projection-free Online Learning

The computational bottleneck in applying online learning to massive data sets is usually the projection step. We present efficient online learning algorithms that eschew projections in favor of much more efficient linear optimization steps using the Frank-Wolfe technique. We obtain a range of regret bounds for online convex optimization, with better bounds for specific cases such as stochastic ...

Designing collaborative learning model in online learning environments

Introduction: Most online learning environments are challenging for the design of collaborative learning activities to achieve high-level learning skills. Therefore, the purpose of this study was to design and validate a model for collaborative learning in online learning environments. Methods: The research method used in this study was a mixed method, including qualitative content analysis and...

Projection-free Distributed Online Learning in Networks

The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient...

Projection-free Distributed Online Learning in Networks: Appendix

Online Optimization in Dynamic Environments

High-velocity streams of high-dimensional data pose significant “big data” analysis challenges across a range of applications and settings. Online learning and online convex programming play a significant role in the rapid recovery of important or anomalous information from these large datastreams. While recent advances in online learning have led to novel and rapidly converging algorithms, the...