Yun Yang
I joined the Statistics Department at University of Illinois UrbanaChampaign in 2018. I was an Assistant Professor in the Statistics Department at Florida State University from 2016 to 2018. Before that, I was a postdoctoral researcher in the EECS department at University of California, Berkeley, working with Martin J. Wainwright and Michael I. Jordan. I obtained my Ph.D. in the Department of Statistical Science at Duke University in 2014. My advisors were David B. Dunson and Surya T. Tokdar. I received my B.S. in Mathematics from Tsinghua University in 2011. My research interests lie broadly in machine learning, scalable Bayes inference and theoretical foundations of high dimensional problems. In particular, I like developing practically efficient methods with strong theoretical support. UIUC PhD students interested in machine learning, highdimensional/nonparametric statistics, or largescale statistical computation, please feel free to contact me. Email
yy84 (at) illinois (dot) edu
Papers * indicating PhD student Implicit regularization via Hadamard product overparametrization in highdimensional linear regression [arXiv] P. Zhao*, Y. Yang and Q. He Gaussian Processes with Errors in Variables: Theory and Computation [arXiv] S. Zhou*, D. Pati, T. Wang, Y. Yang and R. J. Carroll Diffusion Kmeans clustering on manifolds: provable exact recovery via semidefinite relaxations [arXiv] X. Chen and Y. Yang HansonWright inequality in Hilbert spaces with application to Kmeans clustering for nonEuclidean data [arXiv] X. Chen and Y. Yang On the Statistical optimality of variational Bayes. D. Pati, A. Bhattacharya and Y. Yang AISTATS, 2018 A Bayesian Approach to Sequential Monitoring of Nonlinear
Profiles Using Wavelets [link] R. Varbanov*, E. Chicken, A. Linero and Y. Yang Y. Yang, A. Bhattacharya and D. Pati Annals of Statistics, to appear Y. Yang, A. Bhattacharya and D. Pati Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity A. R. Linero and Y. Yang Journal of the Royal Statistical Society: Series B, to appear Y. Yang Q. He, Y. Yang, and B. Zhang Y. Yang, Z. Shang and G. Cheng Bayesian Model Selection Consistency and Oracle Inequality with Intractable Marginal Likelihood [arXiv] Y. Yang and D. Pati Bayesian Fractional Posteriors A. Bhattacharya, D. Pati and Y. Yang Annals of Statistics, to appear CommunicationEfficient Distributed Statistical Inference M. I. Jordan, J. D. Lee and Y. Yang Journal of the American Statistical Association, to appear Joint Estimation of Quantile Planes over Arbitrary Predictor Spaces [R package]
Y. Yang and S. T. Tokdar
Journal of the American Statistical Association, 2017 WaveletBased Bayesian Profile Monitoring R. Varbanov*, E. Chicken, A. Linero and Y. Yang Proceedings of Industrial and Systems Engineering Research Conference, 2017 On the Computational Complexity of HighDimensional Bayesian Variable Selection Y. Yang, M. J. Wainwright and M. I. Jordan Annals of Statistics, 2016 Randomized Sketches for Kernels: Fast and Optimal NonParametric Regression
Y. Yang, M. Pilanci and M. J. Wainwright Annals of Statistics, 2017
Semiparametric Bernsteinvon Mises Theorem: Second Order Studies [arXiv] Y. Yang, G. Cheng and D. B. Dunson Minimax Optimal Bayesian Aggregation [arXiv]
Y. Yang and D. B. Dunson
Minimax Optimal Nonparametric Regression in High Dimensions
Y. Yang and S. T. Tokdar
Annals of Statistics, 2015
Sequential Markov Chain Monte Carlo [arXiv] [C++ code]
Y. Yang and D. B. Dunson Bayesian Manifold Regression
Y. Yang and D. B. Dunson
Annals of Statistics, 2016
Bayesian Crack Detection in Ultra High Resolution Multimodal Images of Paintings
B. Cornelis, Y. Yang, J. T. Vogelstein, A. Dooms, I. Daubechies and D. B. Dunson
18th International Conference on Digital Signal Processing, 2013
Bayesian Conditional Tensor Factorizations for HighDimensional Classification [Matlab code]
Y. Yang and D. B. Dunson
Journal of the American Statistical Association, 2016
A Compact Neural Network for Training Support Vector Machines
Y. Yang, Q. He and X. Hu
Neurocomputing, 2012
