Title:
Lecture 4: Spectral Methods Meets Asymmetry: Two Recent Stories
Lecture 4: Spectral Methods Meets Asymmetry: Two Recent Stories
| dc.contributor.author | Chen, Yuxin | |
| dc.contributor.corporatename | Georgia Institute of Technology. Transdisciplinary Research Institute for Advancing Data Science | en_US |
| dc.contributor.corporatename | Princeton University. Dept. of Electrical Engineering | en_US |
| dc.date.accessioned | 2019-09-18T19:00:47Z | |
| dc.date.available | 2019-09-18T19:00:47Z | |
| dc.date.issued | 2019-09-04 | |
| dc.description | Presented on September 4, 2019 at 11:00 a.m. in the ISyE Main Building, Executive Classroom. | en_US |
| dc.description | Yuxin Chen is an Assistant Professor of Electrical Engineering at Princeton University. His research interests include high-dimensional estimation, machine learning, convex and nonconvex optimization, information theory, statistics, statistical signal processing, network science, and their applications in medical imaging and computational biology. | en_US |
| dc.description | Runtime: 48:56 minutes | en_US |
| dc.description.abstract | This talk is concerned with the interplay between asymmetry and spectral methods. Imagine that we have access to an asymmetrically perturbed low-rank data matrix. We attempt estimation of the low-rank matrix via eigen-decomposition --- an uncommon approach when dealing with non-symmetric matrices. We provide two recent stories to demonstrate the advantages and effectiveness of this approach. The first story is concerned with top-K ranking from pairwise comparisons, for which the spectral method enables un-improvable ranking accuracy. The second story is concern with matrix de-noising and spectral estimation, for which the eigen-decomposition method significantly outperforms the (unadjusted) SVD-based approach and is fully adaptive to heteroscedasticity without the need of careful bias correction. The first part of this talk is based on joint work with Cong Ma, Kaizheng Wang, and Jianqing Fan; the second part of this talk is based on joint work with Chen Cheng and Jianqing Fan. | en_US |
| dc.format.extent | 48:56 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/61860 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | TRIAD Distinguished Lecture Series | en_US |
| dc.subject | Asymmetry | en_US |
| dc.subject | Ranking | en_US |
| dc.subject | Spectral methods | en_US |
| dc.title | Lecture 4: Spectral Methods Meets Asymmetry: Two Recent Stories | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Transdisciplinary Research Institute for Advancing Data Science | |
| local.relation.ispartofseries | Transdisciplinary Research Institute for Advancing Data Science Lectures | |
| relation.isOrgUnitOfPublication | 09be376c-3b5f-4fa8-9e58-6a3595a8353b | |
| relation.isSeriesOfPublication | f402db73-162f-4a58-a9d2-bc56b6a0af52 |
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