NSF PAR Search | NSF Public Access Repository

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Propagation of Information Along the Cortical Hierarchy as a Function of Attention While Reading and Listening to Stories

https://doi.org/10.1093/cercor/bhy282

Regev, Mor; Simony, Erez; Lee, Katherine; Tan, Kean Ming; Chen, Janice; Hasson, Uri (November 2018, Cerebral Cortex)

Full Text Available
Sparse generalized eigenvalue problem: optimal statistical rates via truncated Rayleigh flow

https://doi.org/10.1111/rssb.12291

Tan, Kean Ming; Wang, Zhaoran; Liu, Han; Zhang, Tong (November 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology))

Full Text Available
A convex formulation for high-dimensional sparse sliced inverse regression

https://doi.org/10.1093/biomet/asy049

Tan, Kean Ming; Wang, Zhaoran; Zhang, Tong; Liu, Han; Cook, R Dennis (October 2018, Biometrika)

Full Text Available
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation

Sun, Qiang; Tan, Kean Ming; Liu, Han; Zhang, Tong (July 2018, Proceedings of Machine Learning Research)

We consider the problem of learning high-dimensional Gaussian graphical models. The graphical lasso is one of the most popular methods for estimating Gaussian graphical models. However, it does not achieve the oracle rate of convergence. In this paper, we propose the graphical nonconvex optimization for optimal estimation in Gaussian graphical models, which is then approximated by a sequence of convex programs. Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence. The statistical error introduced by the sequential approximation using a sequence of convex programs is clearly demonstrated via a contraction property. The proposed methodology is then extended to modeling semiparametric graphical models. We show via numerical studies that the proposed estimator outperforms other popular methods for estimating Gaussian graphical models.
more » « less
Full Text Available

Search for: All records