Abstract In this work, we study a new class of risks defined in terms the location and deviation loss distribution, generalizing far beyond classical mean-variance risk functions. The is easily implemented as wrapper around any smooth loss, it admits finite-sample stationarity guarantees for stochastic gradient methods, straightforward to interpret adjust, with close links M-estimators location...

Purpose – By surveying current literature, the purposes of this paper are twofold: to identify current situation of mobile learning (m-learning) adoption and specify the challenges and to identify the factors driving m-learning adoption. Design/methodology/approach – The paper reviews literature related to: m-learning applications and challenging issues and adoption researches on m-learning and...