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• W2808403485 abstract "We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We provide three mistake-bounded algorithms based on three paradigmatic methods for online algorithm design. The algorithm with the strongest guarantee is a quasi-Bayesian classifier which requires $mathcal{O}(t log n)$ time to predict at trial $t$ on an $n$-vertex graph. The fastest algorithm (with the weakest guarantee) is based on a specialist [10] approach and surprisingly only requires $mathcal{O}(log n)$ time on any trial $t$. We also give an algorithm based on a kernelized Perceptron with an intermediate per-trial time complexity of $mathcal{O}(n)$ and a mistake bound which is not strictly comparable. Finally, we provide experiments on simulated data comparing these methods." @default.
• W2808403485 created "2018-06-21" @default.
• W2808403485 creator A5000488844 @default.
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• W2808403485 date "2018-06-17" @default.
• W2808403485 modified "2023-09-27" @default.
• W2808403485 title "Predicting Switching Graph Labelings with Cluster Specialists." @default.
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• W2808403485 hasPublicationYear "2018" @default.
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