Online Prediction at the Limit of Zero Temperature

Part of Advances in Neural Information Processing Systems 28 (NIPS 2015)

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Mark Herbster, Stephen Pasteris, Shaona Ghosh


We design an online algorithm to classify the vertices of a graph. Underpinning the algorithm is the probability distribution of an Ising model isomorphic to the graph. Each classification is based on predicting the label with maximum marginal probability in the limit of zero-temperature with respect to the labels and vertices seen so far. Computing these classifications is unfortunately based on a $\#P$-complete problem. This motivates us to develop an algorithm for which we give a sequential guarantee in the online mistake bound framework. Our algorithm is optimal when the graph is a tree matching the prior results in [1].For a general graph, the algorithm exploits the additional connectivity over a tree to provide a per-cluster bound. The algorithm is efficient as the cumulative time to sequentially predict all of the vertices of the graph is quadratic in the size of the graph.