Optimal structure and parameter learning of Ising models
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Optimal structure and parameter learning of Ising models
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards developing universal reconstruction algorithms which are both computationally efficient and require the minimal amount of expensive data. We introduce a new method, Interaction Screening, which accurately estimates the model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an informationtheoretically optimal number of samples, notably in the lowtemperature regime which is known to be the hardest for learning. The efficacy of Interaction Screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on a real data produced by a DWave quantum computer. This study shows that the Interaction Screening method is an exact, tractable and optimal technique universally solving the inverse Ising problem.
Optimal structure and parameter learning of Ising models
by Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov
https://arxiv.org/pdf/1612.05024v2.pdf
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