New Theory and Algorithms for Scalable Data Fusion
Final rept. 30 Sep 2009-30 Apr 2013
CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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The research performed under this grant served to address the modeling, algorithmic and theoretical challenges associated with problems of large-scale data fusion. Significant research accomplishments included a the development of message passing algorithms for distributed optimization and inference b the formulation and analysis of convex relaxations for estimating low-rank matrices from data c the development of non-parametric methods for solving high-dimensional prediction problems and d the analysis and implementation of methods for graphical model selection.
- Information Science
- Numerical Mathematics