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Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper , we propose a communication-efficient framework, COCOA, that uses local computation in a primal-dual setting to dramatically reduce the amount of necessary communication. We provide a strong(More)
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper , we present a novel generalization of the recent communication-efficient primal-dual framework (COCOA) for(More)
MLI is an Application Programming Interface designed to address the challenges of building Machine Learning algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can(More)
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Despite the importance of sparsity in many big data applications, there are few existing methods for efficient distributed optimization of sparsely-regularized objectives. In this paper, we present a communication-efficient framework for L 1-regularized optimization in distributed environments. By taking a non-traditional view of classical objectives as(More)
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for the distributed environment, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the(More)
Policy makers have recently begun to reconsider involuntary outpatient commitment as a means of enhancing public safety and providing mental health services to people deemed to be noncompliant with treatment. The authors review the therapeutic claims for outpatient commitment and take the position that there is insufficient evidence that it is effective.(More)
— Heating, ventilation, and airconditioning (HVAC) systems use a large amount of energy, and so they are an interesting area for efficiency improvements. The focus here is on the use of semiparametric regression to identify models, which are amenable to analysis and control system design, of HVAC systems. This paper briefly describes two testbeds that we(More)
We compared demographics of subjects diagnosed with frontotemporal degeneration (FTD) at a group of 5 clinics specializing in this non-Alzheimer dementia against those subjects diagnosed at standard Alzheimer disease centers, to determine any differences in referral patterns between such clinics. Of the two major phenotypes of FTD, behavior and language,(More)
Large and complex workflow repositories include a series of interdependent workflows. In this scenario, it becomes hard to estimate the effort required to accomplish changes to workflows. Furthermore, ad-hoc changes may induce side and ripple effects, which ultimately hamper the reliability of the repository. In this paper, we introduce a static(More)