The CoCoA [NIPS 2014] / CoCoA+ [ICML 2015] distributed optimization algorithm developed in a duo of papers with two co-authors from Edinburgh (Martin Tak?, Peter Richtarik) has won the MLconf Industry Impact Student Research Award. The award goes to our coauthor Virginia Smith (UC Berkeley). Other co-authors: M. Jaggi (ETH Zurich), M.I. Jordan (Berkeley), C. Ma (Lehigh), J. Terhorst (UC Berkeley), S. Krishnan (UC Berkeley), T. Hofmann (ETH Zurich).
About the award: "This year, we started a new award program called the MLconf Industry Impact Student Research Award, which is sponsored by Google. This fall, our committee of distinguished ML professionals reviewed several nominations sent in from members of the MLconf community. There were several great researchers that were nominated and the committee arrived at awarding 2 students whose work, they believe, has the potential to disrupt the industry in the future. The two winners that were announced at MLconf SF 2015 are UC Irvine Student, Furong Huang and UC Berkeley Student, Virginia Smith. Below are summaries of their research. Weve invited both researchers to present their work at upcoming MLconf events."
The citation: " Virginia Smiths research focuses on distributed optimization for large-scale machine learning. The main challenge in many large-scale machine learning tasks is to solve an optimization objective involving data that is distributed across multiple machines. In this setting, optimization methods that work well on single machines must be re-designed to leverage parallel computation while reducing communication costs. This requires developing new distributed optimization methods with both competitive practical performance and strong theoretical convergence guarantees. Virginias work aims to determine policies for distributed computation that meet these requirements, in particular through the development of a novel primal-dual framework, CoCoA, which is written on Spark. The theoretical and practical development of CoCoA is an important step for future data scientists hoping to deploy efficient large-scale machine learning algorithms."