Y
Yuan Qi
Researcher at Purdue University
Publications - 29
Citations - 1250
Yuan Qi is an academic researcher from Purdue University. The author has contributed to research in topics: Gaussian process & Bayesian probability. The author has an hindex of 12, co-authored 28 publications receiving 1188 citations.
Papers
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Proceedings ArticleDOI
Using probabilistic generative models for ranking risks of Android apps
Hao Peng,Christopher Gates,Bhaskar P. Sarma,Ninghui Li,Yuan Qi,Rahul Potharaju,Cristina Nita-Rotaru,Ian M. Molloy +7 more
TL;DR: In this paper, the authors introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android applications, and identify three desiderata for an effective risk scoring scheme.
Proceedings Article
Using probabilistic generative models for ranking risks of android apps
Hao Peng,Christopher Gates,Ninghui Li,Yuan Qi,Rahul Potharaju,Cristina Nita-Rotaru,Ian M. Molloy +6 more
TL;DR: This work proposes to use probabilistic generative models for risk scoring schemes, and identifies several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models, and shows that Probabilistic general models significantly outperform existing approaches, and that Naives Bayes models give a promising risk scoring approach.
Proceedings ArticleDOI
Minimizing private data disclosures in the smart grid
TL;DR: This paper considers two well known battery privacy algorithms, Best Effort and Non-Intrusive Load Leveling, and demonstrates attacks that recover precise load change information, which can be used to recover appliance behavior information, under both algorithms.
Journal ArticleDOI
Bayesian Nonparametric Models for Multiway Data Analysis
Zenglin Xu,Feng Yan,Yuan Qi +2 more
TL;DR: This work proposes tensor-variate latent nonparametric Bayesian models based on latent Gaussian or nonlinear covariance functions based on matrices and tensors that achieve significantly higher prediction accuracy than state-of-art tensor decomposition methods and blockmodels.
Proceedings ArticleDOI
Mining roles with noisy data
TL;DR: This work examines role mining with noisy input data and suggests dividing the problem into two steps: noise removal and candidate role generation, and introduces an approach to use (non-binary) rank reduced matrix factorization to identify noise.