Publications in reverse chronological order
Papers listed roughly according to their initial date of appearance.
Directly supervised students are underlined.
2025
S. Zhou. Thresholded Lasso for high dimensional variable selection. 2025. Annals of the Institute of Statistical Mathematics, 2025. 47 Pages. Preprint pdf | Journal Link
S. Zhou. Semidefinite programming relaxations and debiasing for MAXCUT-based clustering. Submitted, 2025. 87 Pages, Preprint arXiv pdf
S. Zhou and K Greenewald. Finite sample rates of convergence for the Bigraphical and Tensor graphical Lasso estimators. 27 Pages. Submitted, 2025. Preprint arXiv pdf
S. Zhou, S. Park, K. Shedden. Kronecker sum covariance models for spatio-temporal data. Submitted, 2025.
2024
S. Zhou and K. Greenewald. Sharper rates of convergence for the tensor graphical Lasso estimator. Proceedings of 2024 IEEE International Symposium on Information Theory (ISIT 2024), Pages 533-588. Athens, Greece. Link to the conference proceedings
S. Zhou. Concentration of measure bounds for matrix-variate data with missing values. Bernoulli 30(1), Pages 198–226, 2024. pdf (Final Version) | Preprint arXiv 2008.03244 | Journal Link
S. Zhou. Debiasing and a local analysis for population clustering using semidefinite programming. January 2024. This paper supersedes an earlier version dated Nov. 2023. Preprint arXiv pdf
2023
S. Zhou. Semidefinite programming on population clustering: a local analysis. Nov. 2023. Preprint arXiv pdf
S. Zhou. Semidefinite programming on population clustering: a global analysis. Jan. 2023. Preprint arXiv pdf
S. Zhou. Thresholded Lasso for high dimensional variable selection. 2023. Preprint arXiv pdf
2022
S. Zhou. Concentration of measure bounds for matrix-variate data with missing values, Feb. 2022. 95 Pages, arXiv Preprint pdf
Originally posted as "The tensor quadratic forms" Preprint arXiv:2008.03244
2021
M. Salloum, D. Jeske, W. Ma, V. Papalexakis, C. Shelton, V. Tsotras, S. Zhou. Developing an interdisciplinary data science program. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 509-515, 2021.
2020
S. Zhou. The tensor quadratic forms. 2020. 71 pages. Preprint arXiv:2008.03244.
2019
R. Fan, B. Jang, Y. Sun, S. Zhou. Precision matrix estimation with noisy and missing Data. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. Link
S. Zhou. Sparse Hanson-Wright inequalities for subgaussian quadratic forms. Bernoulli 25(3), Pages1603-1639, 2019. pdf
K. Greenewald, S. Zhou, A. Hero. The tensor graphical Lasso (TeraLasso). Journal of Royal Statistical Society, Series B 81(5), Pages 901-931, 2019. Journal Link
M. Hornstein, R. Fan, K. Shedden, S. Zhou. Joint mean and covariance estimation with unreplicated matrix-variate data. Journal of the American Statistical Association (Theory and Method), Vol. 114 (526), Pages 682--696, 2019. pdf | R package | Preprint arXiv:1611.04208
2018
M. Hornstein, S. Zhou, K. Shedden. Tensor models for linguistics pitch curve data of native speakers of Afrikaans. Preprint arXiv:1808.05291
2017
K. Greenewald, S. Park, S. Zhou, and A. Giessing. Time-dependent spatially varying graphical models, with application to brain fMRI data analysis. Advances in Neural Information Processing Systems 30, 2017. pdf
M. Rudelson and S. Zhou. Errors-in-variables models with dependent measurements. Electronic Journal of Statistics, Vol. 11(1), Pages 1699-1797, April 2017. pdf
S. Park, X. He, and S. Zhou. Dantzig-type penalization for multiple quantile regression with high dimensional covariates. Statistica Sinica, Vol. 27(4), Pages 1619-1638.
S. Park, K. Shedden, and S. Zhou. Non-separable covariance models for spatio-temporal data, with applications to neural encoding analysis. Preprint arXiv:1705.05265, May 2017.
2016
T.H. Chan, A. Gupta, B.M. Maggs and S. Zhou. On hierarchical routing in doubling metrics. ACM Transactions on Algorithms (TALG) Volume 12(4), September 2016. Journal Link/ pdf
M. Hornstein, R. Fan, K. Shedden, S. Zhou. Joint mean and covariance estimation with unreplicated matrix-variate data. Preprint arXiv:1611.04208, November 2016.
2015
M. Rudelson and S. Zhou. High dimensional errors-in-variables models with dependent measurements. Preprint arXiv:1502.02355
2014
S. Zhou. Gemini: Graph estimation with matrix variate normal instances. Annals of Statistics, Volume 42(2), Pages 532-562, 2014. Journal Link
2013
M. Rudelson and S. Zhou. Reconstruction from anisotropic random measurements. IEEE Transactions on Information Theory, Volume 59(6), Pages 3434--3447. pdf / talk slides
T. Tsiligkaridis, A. Hero, and S. Zhou. On convergence of Kronecker graphical Lasso algorithms. IEEE Transactions on Signal Processing, Volume 61(7), Pages 1743--1755. pdf
A. Kalaitzis, J. Lafferty, N. Lawrence, S. Zhou. The Bigraphical Lasso. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA (ICML 2013). PMLR 28(3), Pages 1229--1237, 2013. Link to Conf Proceedings.
2012
S. Zhou. Gemini: Graph estimation with matrix variate normal instances. Sept., 2012. Preprint arXiv:1209.5075
M. Rudelson and S. Zhou. Reconstruction from anisotropic random measurements. Annual Conference on Learning Theory (COLT) 2012. Edinburgh, Scotland.
2011
S. Zhou, P. Rutimann, M. Xu, P, Buhlmann. High-dimensional covariance estimation based on Gaussian graphical models. Journal of Machine Learning Research, 2011. Journal Link / pdf
S. van de Geer, P. Buhlmann, S. Zhou. The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso). Electronic Journal of Statistics 2011, Vol. 5, Pages 688-749. Journal Link
2010
L. Wasserman and S. Zhou. A Statistical framework for differential privacy. Journal of the American Statistical Association, Vol. 105(489), Pages 375--389. Appeared as a featured article pdf
S. Zhou and S. Rao. Edge disjoint paths in moderately connected graphs. SIAM Journal on Computing, Vol.39(5), Pages 1856-1887, January, 2010. Journal version.
S. Zhou, J. Lafferty, L. Wasserman. Time varying undirected graphs. Machine Learning Journal. Vol 80, Numbers 2--3, Pages 295--319, Sep. 2010.
S. Zhou. Thresholded Lasso for high dimensional variable selection and statistical estimation. February 8, 2010. Preprint arXiv:1002.1583.
2009
S. Zhou. Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation. Advances in Neural Information Processing Systems 22, Pages 2304--2312, December 2009.
A. Blum, A. Coja-Oghlan, A. Frieze, and S. Zhou. Separating populations with wide data: a spectral analysis. Electronic Journal of Statistics, Volume 3, Pages 76--113, 2009. Journal Link/ pdf
S. Zhou, J. Lafferty, L. Wasserman. Compressed and privacy sensitive sparse regression. Transactions on Information Theory, Volume 55(2), Pages 846--866, February 2009. Preprint arXiv:0706.0534
S. Zhou, K. Ligett, L. Wasserman. Differential privacy with compression. 2009 IEEE International Symposium on Information Theory. Seoul, Korea, June -- July 2009. Preprint arXiv:0901.1365.
S. Zhou. Restricted eigenvalue conditions on Subgaussian random matrices. December, 2009. Preprint arXiv:0912.4045
S. Zhou, S. van de Geer, P. Buhlmann. Adaptive Lasso for high dimensional regression and Gaussian graphical modeling. March 2009. Preprint arXiv:0903.2515
2008
S. Zhou, J. Lafferty, L. Wasserman. Time varying undirected graphs. The 21st Annual Conference on Learning Theory (COLT 2008). Helsinki, Finland, July 2008. Preprint arXiv:0802.2758
S. Zhou. Learning balanced mixtures of discrete distributions with small sample. February 2008. Preprint arXiv:0802.1244
2007
S. Zhou, J. Lafferty, L. Wasserman. Compressed regression. IEEE Trans. Info. Theory, Vol.55 (2), Pages 846--866, February 2009.
Preliminary version in the Twenty-First Annual Conference on Neural Information Processing Systems. Vancouver, BC, Canada. December 2007. Preprint arXiv:0706.0534
A. Blum, A. Coja-Oghlan, A. Frieze, and S. Zhou. Separating populations with wide data: a spectral analysis. Electronic Journal of Statistics, Volume 3, Pages 76-113, 2009.
Preliminary version in the 18th International Symposium on Algorithms and Computation (ISAAC 2007). Sendai, Japan, December 2007.
K. Chaudhuri, E. Halperin, S. Rao, and S. Zhou. A rigorous analysis of population stratification with limited data. ACM-SIAM Symposium on Discrete Algorithms (SODA) 2007. January 2007.
2006
S. Zhou and S. Rao. Edge disjoint paths in moderately connected graphs. SIAM Journal on Computing, Vol.39(5), Pages 1856-1887, January 27, 2010.
Preliminary version in International Colloquium on Automata, Languages and Programming (ICALP 06). Venice, Italy. July 2006.
S. Zhou. Routing, disjoint paths, and classification. Carnegie Mellon University Ph.D. Dissertation CMU-PDL-06-109, August 2006. (This contains results in my SODA05, ICALP06, SODA07 papers and Preprint arXiv:0802.1244.)
2005 and before
T.H. Chan, A Gupta, B.M. Maggs, S. Zhou. On hierarchical routing in doubling metrics. ACM Transactions on Algorithms (TALG), Volume 12(4). September 2016.
Preliminary version in ACM-SIAM Symposium on Discrete Algorithms (SODA 2005). Vancouver, BC, Canada. January 2005.
TR CMU-PDL-04-106 contains an updated version on routing. December 2004
S. Zhou, GR Ganger, P Steenkiste. Balancing locality and randomness in DHTs. Carnegie Mellon University Technical Report CMU-CS-03-203. November 2003. Abstract
S. Zhou, GR Ganger, P Steenkiste. Location-based node IDs: Enabling explicit locality in DHTs. Carnegie Mellon University Technical Report CMU-CS-03-171. September 2003. Abstract
D.S. Tan, S. Zhou, J. Ho, J. Mehta, H. Tanabe. Design and evaluation of an individually simulated mobility model in wireless Ad Hoc networks. Communication Networks and Distributed Systems Modeling and Simulation Conference 2002. San Antonio, TX.
Unpublished Manuscripts
Differential Privacy for Continuous Data, with L Wasserman. July 2008.
Quantization and the Privacy-Accuracy Tradeoff, with S Fienberg, Y Nardi, A Rinaldo, L Wasserman. June 2008.