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Wiley Interdisciplinary Reviews:
WIREs Computational Statistics
Volume 12 Issue 5 (September 2020)
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Multiple and multilevel graphical models
Published Online: Feb 03 2020
DOI: 10.1002/wics.1497
An illustration of the joint multilevel graphical models: the higher level networks among big circles and the lower level networks among small circles within big circles.
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Deep learning: Computational aspects
Published Online: Jun 09 2020
DOI: 10.1002/wics.1500
In this article, we review computational aspects of deep learning (DL). DL uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high‐dimensional input–output models. Training a DL architecture is computationally intensive, and efficient linear algebra library is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive datasets.
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Adversarial machine learning for cybersecurity and computer vision: Current developments and challenges
Published Online: Apr 21 2020
DOI: 10.1002/wics.1511
Poisoning attack contaminates the training data to render a classifier useless; evasion attack generates adversarial samples at test time; membership inference attack and model inversion attack aim to infer information about data points used in the training process.
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Individualized inference through fusion learning
Published Online: Jan 13 2020
DOI: 10.1002/wics.1498
Prediction performance of individual Value‐at‐Risk estimation for S&P 500 stocks by aggregating other stocks with similar Fama‐French coefficients through individualized Group learning. The aggregation is controlled by a bandwidth parameter. The two limits at zero bandwidth and infinite bandwidth are equivalent to individual estimation and the classical fusion learning estimation, correspondingly. Our individualized fusion learning approach, by choosing the optimal bandwidth, has a superior performance than the other classical approaches.
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Focus Article

Clustering of multivariate geostatistical data
Published Online: Mar 26 2020
DOI: 10.1002/wics.1510
(a and d) Classical spectral clustering; (b and e) spectral clustering based on a spatial similarity measure; (c and f) spectral clustering based on spatial contiguity constraints.
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