# Resources

I'll put my code, presentation slides, useful links, and various other useful resources here.

### My journal club/discussion class presentations

- "Stochastic Subgradient Descent for Nuclear Norm Regularization" [slides]
- Non-negative Matrix Factorization(NMF) and "A-Optimal Non-Negative Projection for Image Representation" [slides]
- Differential privacy tutorial [slides]
- Deep Learning and "Sparse modeling of human actions from motion imagery" [slides]
- Subspace Clustering with missing data: "High rank matrix completion" [slides]
- Discussion of Wiberg L1 (CVPR10 Best Paper) [slides]

### Larry Wasserman's blog: Normal Deviate

Cool blog from the CMU statistician on statistics and machine learning topics.

### John Langford's blog: hunch.net

A renowned machine learning theory blog. A few good/interesting posts per month. To have a flavor, check out the article: Adversarial Academia.

### Matrix Factorization Jungle

A comprehensive site that keeps updating the state-of-the-art algorithms, theory and evaluations in MF related fields, including: Matrix Completion, Matrix Recovery(RPCA), Compressive Sensing, Dictionary learning, Non-negative Matrix Factorization and etc.

### Nuit Blanche blog on Compressive Sensing and Matrix Factorization

The maintainer of Matrix Factorization Jungle (Igor Carron), articles are faster than updates on the summary site.

### Compressive sensing resources

An almost thorough list of compressive sensing papers, reviews and tutorials.

### Ma Yi's Low-rank matrix recovery & completion page

A list of papers on nuclear norm based convex methods for low-rank matrix. Useful code samples of Augmented Lagrange Multiplier methods for RPCA.