In this paper we develop dual free SDCA with adaptive probabilities for regularized empirical risk minimization. This extends recent work of Shai Shalev-Shwartz [SDCA without Duality, arXiv:1502.06177] to allow non-uniform selection of ”dual” coordinate in SDCA. Moreover, the probability can change over time, making it more efficient than uniform selection. Our work focuses on generating adapti...

risk management is one of the most important aspects of project management that identifies, assesses and responds to project risks. although many papers have been published in project risk response, presented tools and methods are poor. hence, in this paper, we present an optimization model to respond project risk that seeks to optimize two key criteria of project: cost and time. the proposed m...

We suggest a penalty function to be used in various problems of structural risk minimization. This penalty is data dependent and is based on the sup-norm of the so called Rademacher process indexed by the underlying class of functions (sets). The standard complexity penalties, used in learning problems and based on the VCdimensions of the classes, are conservative upper bounds (in a probabilist...

Abstract This paper investigates robust versions of the general empirical risk minimization algorithm, one core techniques underlying modern statistical methods. Success is based on fact that for a ‘well-behaved’ stochastic process $\left \{ f(X), \ f\in \mathscr F\right \}$ indexed by class functions $f\in F$, averages $\frac{1}{N}\sum _{j=1}^N f(X_j)$ evaluated over sample $X_1,\ldots ,X_N$ i...