In external beam radiotherapy (EBRT), patient setup verification over the entire course of fractionated treatment is necessary for accurate delivery of specified dose to the tumor. We develop an information theoretic minimax entropy registration framework for patient setup verification using portal images and the treatment planning 3D CT data set. Within this framework we propose to simultaneou...

Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. We empirically show that it is superior over distributed online learning via ...

We study and develop (stochastic) primal–dual block-coordinate descent methods based on the method of Chambolle and Pock. Our methods have known convergence rates for the iterates and the ergodic gap: O(1/N 2) if each each block is strongly convex, O(1/N ) if no convexity is present, and more generally a mixed rate O(1/N 2) + O(1/N ) for strongly convex blocks, if only some blocks are strongly ...

Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet Things (IoT) based intelligent ubiquitous computing. For fast-increasing applications data amounts, distributed promising emerging paradigm since it often impractical or inefficient to share/aggregate centralized location from distinct ones. This paper studies the problem training an ML model...