Herbster, M;
Robinson, J;
(2019)
Online Prediction of Switching Graph Labelings with Cluster Specialists.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alche-Buc, F and Fox, E and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 32 (NIPS 2019).
Neural Information Processing Systems (NIPS): Vancouver, Canada.
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Abstract
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 [11] 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.
Type: | Proceedings paper |
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Title: | Online Prediction of Switching Graph Labelings with Cluster Specialists |
Event: | 33rd Conference on Neural Information Processing Systems (NeurIPS) |
Location: | Vancouver, CANADA |
Dates: | 08 December 2019 - 14 December 2019 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://papers.nips.cc/book/advances-in-neural-inf... |
Language: | English |
Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10110105 |
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