2 citations · 2 across the 6 of their papers we have counts for
6 papers
Automatic selection of hyper-parameters via the use of softened profile likelihood
Gengyang Chen, Mu Zhu
We extend a heuristic method for automatic dimensionality selection, which maximizes a profile likelihood to identify "elbows" in scree plots. Our extension enables researchers to…
Restricted Tweedie Stochastic Block Models
Jie Jian, Mu Zhu, Peijun Sang
The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. Howeve…
Two Gaussian regularization methods for time-varying networks
Jie Jian, Peijun Sang, Mu Zhu
We model time-varying network data as realizations from multivariate Gaussian distributions with precision matrices that change over time. To facilitate parameter estimation, we re…
Dependence model assessment and selection with DecoupleNets
Marius Hofert, Avinash Prasad, Mu Zhu
Neural networks are suggested for learning a map from -dimensional samples with any underlying dependence structure to multivariate uniformity in dimensions. This map, term…
Threshold-free Evaluation of Medical Tests for Classification and Prediction: Average Precision versus Area Under the ROC Curve
Wanhua Su, Yan Yuan, Mu Zhu
When evaluating medical tests or biomarkers for disease classification, the area under the receiver-operating characteristic (ROC) curve is a widely used performance metric that do…
On the underestimation of model uncertainty by Bayesian K-nearest neighbors
Wanhua Su, Hugh Chipman, Mu Zhu
When using the K-nearest neighbors method, one often ignores uncertainty in the choice of K. To account for such uncertainty, Holmes and Adams (2002) proposed a Bayesian framework…