Here you can enter your suggestions for the reading group.

Papers in all fields of Machine Learning are welcomed!

Technion | Israel Institute of Technology

Here you can enter your suggestions for the reading group.

Papers in all fields of Machine Learning are welcomed!

Hessian Free

http://link.springer.com/chapter/10.1007%2F978-3-642-35289-8_27?LI=true

Randomized Smoothing for Stochastic Optimization (or as a topic: algorithms for non smooth optimization)

Gaussian Processes for Machine Learning

Rasmussen and Williams

Chapters 2 and 5 of the book cover Gaussian process regression, and are an easy read.

Dimensionality reduction

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1. Overview:

[PDF] Dimensionality reduction: A comparative review

LJP Van der Maaten, EO Postma… – Journal of Machine …, 2009 – Citeseer

2. Semi-supervised local Fisher discriminant analysis for dimensionality reduction

M Sugiyama, T Idé, S Nakajima, J Sese – Machine learning, 2010

3. A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models

ND Lawrence – The Journal of Machine Learning Research, 2012

We can also instead try to understand rough set theory (I have no idea what that is):

Dimensionality reduction based on rough set theory: A review

And follow a few of the mentioned papers.

http://arxiv.org/pdf/1304.7045v1.pdf

The results seem impressive and the main idea looks quite simple and elegant. I volunteer to lead the discussion.

For a while, I’ve been curious to understand results in sparse signal recovery / compressed sensing / matrix completion. Especially, the general themes of “dual certificates” and “golfing schemes” feature as key ingredients in proofs of optimization programs. A couple of influential papers in this vein-

D. Gross. Recovering low-rank matrices from few coefficients in any basis. Available at http://arxiv.

org/abs/0910.1879, 2009.

E. Candes and Y. Plan. A probabilistic RIP-less theory of compressed sensing. Available at http://arxiv.org/abs/1011.3854, 2010.

NIPS2013

post the name of paper you wish to present as a reply to this post.

paper presentation should be no longer than 10 minutes.