Assistant Professor, Department of Mathematics
Office: MSB building 202G
- Structured Robust Covariance Estimation, Ami Wiesel and Teng Zhang, Foundations and Trends in Signal Processing: Vol. 8: No. 3, pp 127-216, 2015.
- Disentangling Orthogonal Matrices, Teng Zhang and Amit Singer, accepted to Linear Algebra and its Applications.
- Flexible Expectile Regression in Reproducing Kernel Hilbert Space, Yi Yang, Teng Zhang and Hui Zou, accepted to Technometrics. R-package
- A Majorization-Minimization Algorithm for Computing the Karcher Mean of Positive Definite Matrices, Teng Zhang, SIAM Journal on Matrix Analysis and Applications, 38-2 (2017), pp. 322-342. (Matlab code)
- Spectral Clustering Based on Local PCA, Ery Arias-Castro, Gilad Lerman, and Teng Zhang. (Matlab code), accepted to Journal of Machine Learning Research.
- Denoising and Covariance Estimation of Single Particle Cryo-EM Images. Tejal Bhamre, Teng Zhang and Amit Singer, Journal of Structural Biology, Volume 195, Issue 1, July 2016, Pages 72–81.
- Marchenko-Pastur Law for Tyler’s M-estimator, Teng Zhang, Xiuyuan Cheng and Amit Singer, Journal of Multivariate Analysis, Volume 149, July 2016, 114–123.
- Robust subspace recovery by Tyler’s M-estimator, Teng Zhang, Information and Inference: A Journal of the IMA, 2016, 5 (1): 1-21.
- Robust computation of linear models by convex relaxation, Gilad Lerman, Michael McCoy, Joel A. Tropp, and Teng Zhang. Foundation of Computational Mathematics. 2015 15 (2): 363-410. (Matlab code)
- Sparse Precision Matrix Estimation via Positive Definite Constrained Minimization of $\ell_1$ Penalized D-Trace Loss. Teng Zhang and Hui Zou. Biometrika, 2014, 101 (1): 103-120. (Matlab code)
- lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers, Gilad Lerman and Teng Zhang. Constructive Approximation, 2014, 40:329–385.
- A Novel M-Estimator for Robust PCA, Teng Zhang and Gilad Lerman. Journal of Machine Learning Research, 2014, 15(1): 749-808. (Matlab code)
- Multivariate Generalized Gaussian Distribution: Convexity and Graphical Models, Teng Zhang, Ami Wiesel, Maria Sabrina Grec. IEEE Transactions on Signal Processing, 2013, Vol. 61, Issue. 16, 4141-4148.
- Hybrid Linear Modeling via Local Best Fit Flats, Teng Zhang, Arthur Szlam, Yi Wang and Gilad Lerman. International Journal of Computer Vision, 2012, Vol. 100, Issue.3, 217-240 (Matlab code and supplementary material)
- Robust recovery of multiple subspaces by geometric lp minimization, Gilad Lerman and Teng Zhang, Annals of Statistics 2011, Vol. 39, No. 5, 2686-2715.
- Automatic diagonal loading for Tyler’s robust covariance estimator, Teng Zhang and Ami Wiesel, 2016 IEEE Workshop on Statistical Signal Processing (SSP 2016).
Orthogonal Matrix Retrieval in Cryo-Electron Microscopy, Tejal Bhamre, Teng Zhang and Amit Singer. 2015 IEEE International Symposium on Biomedical Imaging.
Robust Stochastic Principal Component Analysis, John Goes, Teng Zhang, Raman Arora and Gilad Lerman. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS 2014).
Randomized Hybrid Linear Modeling by Local Best-fit Flats, Teng Zhang, Arthur Szlam, Yi Wang and Gilad Lerman. Proceedings of the 2010 IEEE 23rd International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1927-1934. (Matlab code and supplementary material)
Median K-Flats for Hybrid Linear Modeling with Many Outliers, Teng Zhang, Arthur Szlam and Gilad Lerman. Proc. of 2nd IEEE International Workshop on Subspace Methods (Subspace 2009), pp. 234-241 (2009). (Matlab code)
- Spectral clustering in the dynamic stochastic block model, Marianna Pensky and Teng Zhang.
- A Well-Tempered Landscape for Non-convex Robust Subspace Recovery, Tyler Maunu, Teng Zhang, and Gilad Lerman.
- Anisotropic twicing for single particle reconstruction using autocorrelation analysis. Tejal Bhamre, Teng Zhang, and Amit Singer.
- A note on the non-commutative arithmetic-geometric mean inequality.