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Poster
Online Prediction of Switching Graph Labelings with Cluster Specialists
Mark Herbster · James Robinson

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #55
in Algorithms -- Online Learning »

We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We present an algorithm based on a specialist approach; we develop the machinery of cluster specialists which probabilistically exploits the cluster structure in the graph. Our algorithm has two variants, one of which surprisingly only requires O(log n) time on any trial t on an n-vertex graph, an exponential speed up over existing methods. We prove switching mistake-bound guarantees for both variants of our algorithm. Furthermore these mistake bounds smoothly vary with the magnitude of the change between successive labelings. We perform experiments on Chicago Divvy Bicycle Sharing data and show that our algorithms significantly outperform an existing algorithm (a kernelized Perceptron) as well as several natural benchmarks.

Keywords: Online Learning · Algorithms
[ Paper]  [ Poster] 

Author Information

Mark Herbster (University College London)
James Robinson (UCL)

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