Projection-free Online Learning

Projection-free Distributed Online Learning in Networks: Appendix

Online Compact Convexified Factorization Machine

Factorization Machine (FM) is a supervised learning approach with a powerful capability of feature engineering. It yields state-ofthe-art performance in various batch learning tasks where all the training data is made available prior to the training. However, in real-world applications where the data arrives sequentially in a streaming manner, the high cost of re-training with batch learning al...

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...

A Bayesian Approach to Online Learning

Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we sh...

Correlation between Online Learner Readiness with Psychological Distress related to e-Learning among Nursing and Midwifery Students during COVID-19 pandemic

Introduction: With the sudden shift of face-to-face education to e-learning during the COVID-19 pandemic, awareness of learnerschr('39') readiness for online learning and its impact on studentschr('39') psychological distress related to e-learning is important for teachers, counselors, and educational planners. Therefore, the present study was conducted to investigate the correlation between on...