Combinatorial Online Learning and Its Applications
These researchers investigate efficient online learning algorithms for combinatorial problems as well as their applications on a variety of real-world domains. Online algorithms maintain adaptive models for data over time without making any statistical assumptions about the data. Problems of particular interest include high-dimensional sparse optimization problems, graph-structured linear programs, and convex programs with semi-definiteness constraints. The corresponding models will be applied to a variety of real world domains including climate sciences, forest ecology, aviation safety, recommendation systems and social media analytics.
A bibliography of this group’s publications is attached.
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