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:

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.