Generalized Stochastic Gradient Learning

We introduce a class of gradient adaptive stepsize algorithms for quaternion valued adaptive filtering based on three- and four-dimensional vector sensors. This equips the recently introduced quaternion least mean square (QLMS) algorithm with…

Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current pseudo-residuals by least squares at each iteration. The pseudo-residuals are the gradient of the loss…

In this paper we perform an inâ''depth investigation of relative merits of two adaptive learning algorithms with constant gain, Recursive Least Squares (RLS) and Stochastic Gradient (SG), using the Phelps model of monetary policy as a testing…

In batch learning, stability together with existence and uniqueness of the solution corresponds to well-posedness of Empirical Risk Minimization (ERM) methods; recently, it was proved that CVloo stability is necessary and sufficient for…

In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the…

We derive new adaptive step size algorithms for two general classes of modified stochastic gradient adaptive filters that include the sign-error, sign-data, sign-sign, and normalized gradient adaptive filters as specific cases. These…

The natural gradient method has an ideal dynamic behavior which resolves the slow learning speed of the standard gradient descent method caused by plateaus. However, it is required to calculate the Fisher information matrix and its inverse, which…

Stochastic techniques have assumed a prominent role in computer graphics, because of their success in modeling a variety of complex and natural phenomena. This paper describes the basis for techniques such as stochastic subdivision in the theory of…

Page 1. Parallelized Stochastic Gradient Descent Martin A. Zinkevich Yahoo! Labs Sunnyvale, CA 94089 mazyahoo-inc.com Markus Weimer Yahoo! Labs Sunnyvale, CA 94089 weimeryahoo-inc.com Alex Smola Yahoo! Labs Sunnyvale, CA 94089 smolayahoo-inc.com ...

Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are sometimes used to overcome these difficulties. We analyze three online training methods used with a linear perceptron:…