Projects can be done individually or in groups of 2. If you are taking the course for credit, please pair up with another student taking the course for credit.
Select one of the papers listed below, and send me an email about your selection, and also let me know your project team-mate if you have one. Once you have selected a paper, your name will appear next to the paper in [square brackets]. Once a paper is taken, other students will have to select a different paper for their project.
Your grade will depend on:
Your demonstration of your understanding of the paper (through the presentation, report, simulations, etc)
The presentation (organization, clarity, content)
The report (details of proofs, simulation results, critical comments)
Any new ideas/results you can add, however small.
Please use this style file for writing your reports.
Initial project presentation (12 mins per group, 3 mins for questions and changing between speakers): Mar. 6th and 8th, 2013. For your presentation:
First slide: Title of your project (paper), along with its complete reference, and your names, departments, and email addresses.
Present the problem that the paper considers
Present the main message that the paper conveys, in your opinion. Why is this paper important?
Make a proposal on what you will show in the final presentation and report
Any other items you wish to include – you have 12 mins!
Initial project presentation schedule:
Mar. 6th, 2013: Projects 1 through 6 below.
Mar. 8th, 2013: Projects 7 through 11 below.
Projects
Robust PCA:
Emmanuel J. Candes, Xiaodong Li, Yi Ma, and John Wright, “Robust Principal Component Analysis?” here. [Sk. Mohammadul Haque]
Mixed-norm penalties:
Peng Zhao, Guilherme Rocha, Bin Yu, “The composite absolute penalties family for grouped and hierarchical variable selection”, here. [Saurabh]
Sahand N. Negahban and Martin J. Wainwright, “Simultaneous support recovery in high dimensions: Benefits and perils of block $\ell_1/\ell_\infty$-regularization”, here. [Venu/Parthajit]
G. Obozinski, M. J. Wainwright, and M. I. Jordan, “Union support recovery in high-dimensional multivariate regression”, here.
Graph selection:
Nicolai Meinhausen, Peter Buhlmann, “High Dimensional Graphs and variable selection with LASSO”, here.
David L Donoho, Arian Maleki, Andrea Montanari, “Message Passing Algorithms for Compressed Sensing”, here. [Lakshminarasimhan]
Boolean CS:
G. Atia and V. Saligrama, “Boolean compressed sensing and noisy group testing”, here. [Anuva]
Distributed CS:
Dror Baron, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, and Richard G. Baraniuk, “Distributed Compressive Sensing”, here.
Information theoretic results:
Martin J. Wainwright, “Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting”, here. [Subhadip/Abhijit Bhattacharya]
Alyson K. Fletcher, Vivek Goyal, “Necessary and Sufficient Conditions on Sparsity Pattern Recovery”, here. [Aparna/Deepa]
Linda M. Davis, Stephen V. Hanly, Paul Tune, Sibi Raj Bhaskaran, “Channel estimation and user selection in the MIMO broadcast channel”, here. [Geethu/Kaushik]
Adaptive CS:
Ery Arias-Castro, Emmanuel J. Candes and Mark A. Davenport, “On the Fundamental Limits of Adaptive Sensing”, here.
Online LASSO:
Pierre J. Garrigues and Pierre J. Garrigues, “An Homotopy Algorithm for the Lasso with Online Observations”, here. [Sanjeev K./Manoj]
Dmitry M. Malioutov, Sujay R. Sanghavi, and Alan S. Willsky, “Sequential Compressed Sensing”, here. [Neeraj/Ranjani]
Uncertainty Principles:
Deanna Needell and Roman Vershynin, “Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit”, here. [Tulasi Ram/Vijay Girish]