Hessian Approximated Multiple Subsets Iteration (HAMSI) google
We propose HAMSI, a provably convergent incremental algorithm for solving large-scale partially separable optimization problems that frequently emerge in machine learning and inferential statistics. The algorithm is based on a local quadratic approximation and hence allows incorporating a second order curvature information to speed-up the convergence. Furthermore, HAMSI needs almost no tuning, and it is scalable as well as easily parallelizable. In large-scale simulation studies with the MovieLens datasets, we illustrate that the method is superior to a state-of-the-art distributed stochastic gradient descent method in terms of convergence behavior. This performance gain comes at the expense of using memory that scales only linearly with the total size of the optimization variables. We conclude that HAMSI may be considered as a viable alternative in many scenarios, where first order methods based on variants of stochastic gradient descent are applicable. …

Penalized Adaptive Weighted Least Squares Regression (PWLS) google
To conduct regression analysis for data contaminated with outliers, many approaches have been proposed for simultaneous outlier detection and robust regression, so is the approach proposed in this manuscript. This new approach is called “penalized weighted least squares” (PWLS). By assigning each observation an individual weight and incorporating a lasso-type penalty on the log-transformation of the weight vector, the PWLS is able to perform outlier detection and robust regression simultaneously. A Bayesian point-of-view of the PWLS is provided, and it is showed that the PWLS can be seen as an example of Mestimation. Two methods are developed for selecting the tuning parameter in the PWLS. The performance of the PWLS is demonstrated via simulations and real applications. …

Microsoft Project Oxford google
Set of technologies dubbed Project Oxford that allows developers to create smarter apps, which can do things like recognize faces and interpret natural language even if the app developers are not experts in those fields. “If you are an app developer, you could just take the API capabilities and not worry about the machine learning aspect,” said Vijay Vokkaarne, a principal group program manager with Bing, whose team is working on the speech aspect of Project Oxford. …

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