Bowei Yan

Bowei Yan
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Bowei Yan

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Pub Categories

Statistics - Machine Learning (3)
Statistics - Methodology (3)
Computer Science - Learning (1)
Mathematics - Statistics (1)
Statistics - Applications (1)
Statistics - Theory (1)

Publications Authored By Bowei Yan

Brain mapping is an increasingly important tool in neurology and psychiatry researches for the realization of data-driven personalized medicine in the big data era, which learns the statistical links between brain images and subject level features. Taking images as responses, the task raises a lot of challenges due to the high dimensionality of the image with relatively small number of samples, as well as the noisiness of measurements in medical images. In this paper we propose a novel method {\it Smooth Image-on-scalar Regression} (SIR) for recovering the true association between an image outcome and scalar predictors. Read More

We develop a finite-sample goodness-of-fit test for \emph{latent-variable} block models for networks and test it on simulated and real data sets. The main building block for the latent block assignment model test is the exact test for the model with observed blocks assignment. The latter is implemented using algebraic statistics. Read More

We present a framework and analysis of consistent binary classification for complex and non-decomposable performance metrics such as the F-measure and the Jaccard measure. The proposed framework is general, as it applies to both batch and online learning, and to both linear and non-linear models. Our work follows recent results showing that the Bayes optimal classifier for many complex metrics is given by a thresholding of the conditional probability of the positive class. Read More

Community detection in networks is an important problem in many applied areas. In this paper, we investigate this in the presence of node covariates. Recently, an emerging body of theoretical work has been focused on leveraging information from both the edges in the network and the node covariates to infer community memberships. Read More

Clustering is one of the most important unsupervised problems in machine learning and statistics. Among many existing algorithms, kernel k-means has drawn much research attention due to its ability to find non-linear cluster boundaries and its inherent simplicity. There are two main approaches for kernel k-means: SVD of the kernel matrix and convex relaxations. Read More