We investigate the stochastic optimization problem of minimizing population risk, where loss defining risk is assumed to be weakly convex. Compositions Lipschitz convex functions with smooth maps are primary examples such losses. analyze estimation quality nonsmooth and nonconvex problems by their sample average approximations. Our main results establish dimension-dependent rates on subgradient...

Purpose This study is intended to compare dose-volume parameters evaluated using different forward planning- optimization techniques, involving two applicator systems in intracavitary brachytherapy for cervical cancer. It looks for the best applicator-optimization combination to fulfill recommended dose-volume objectives in different high-dose-rate (HDR) fractionation schedules. Material and ...

This paper develops methods of distributed Bayesian hypothesis tests for fault detection and diagnosis that are based on belief propagation and optimization in graphical models. The main challenges in developing distributed statistical estimation algorithms are i) difficulties in ensuring convergence and consensus for solutions of distributed inference problems, ii) increasing computational cos...

The application of classical optimization techniques to Graphical Models has led to specialized derivations of powerful paradigms such as the class of EM algorithms, variational inference, max-margin and maximum entropy learning. This view has also sustained a conceptual bridge between the research communities of Graphical Models, Statistical Physics and Numerical Optimization. The optimization...

This paper addresses the problem of scalable optimization for l1-regularized conditional Gaussian graphical models. Conditional Gaussian graphical models generalize the well-known Gaussian graphical models to conditional distributions to model the output network influenced by conditioning input variables. While highly scalable optimization methods exist for sparse Gaussian graphical model estim...