Category Archives: Probabilistic Graphical models

Compilation of topics since May

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Sent September 9:
This week we will go over the paper:
It talks about primal-dual averaging, one of the methods mentioned in the review we covered which we didn’t quite work out.
Sent August 26:
Reminder – As we discussed last week, we will talk about the second part of the optimization lecture on Thursday.

Sent August 13:
Sent August 5:
I want to dedicate a few sessions to the area of optimization in ML. The idea is to cover new results but also try to make a “map” of the area, and make the connections between the fields.
To bring us all up to some level, this week, instead of  reading a paper on a specific algorithmic/theoretical result, I thought we should read a review on the subject. I couldn’t find a good one written but found a nice nips tutorial on the subject (so you don’t even have to read:)).
Sent July 28:
from ICML 2013.
Sent July 14:
This week (Thursday @14:30) we will continue with Gaussian Processes. The subject will be the paper from ICML2011: http://www.icml-2011.org/papers/323_icmlpaper.pdf,  which applies GPs to Reinforcement Learning.
Also, here a motivational video (learning this task previously demanded hundreds of trials, this algorithm does it in 7):
Sent July 3:
It was suggested that we do a couple of sessions on Gaussian Processes. For next week, please read Chapters 2 and 5 of the book Gaussian Processes for Machine Learning, available at http://www.gaussianprocess.org/gpml/.
Sent June 16:
This week we will go over the paper:
“A Provably Efficient Algorithm for Training Deep Networks” http://arxiv.org/abs/1304.7045

Sent June 9:

Odalric will lead the discussion on the paper –

“Follow the Leader If You Can, Hedge If You Must”
http://arxiv.org/pdf/1301.0534v2.pdf, by Steven de Rooij, Tim van Erven, Peter D. Grünwald, Wouter M. Koolen.
This paper considers the online learning setting and tries to find a way to optimally
tune the Hedge algorithm so as to get an (really) adaptive algorithm.

A quick reference to the Hedge Algorithm: http://onlineprediction.net/n=Main.HedgeAlgorithm

Some motivation why this setting is useful, can be found in http://hal.archives-ouvertes.fr/docs/00/71/51/77/PDF/Devaine-Goude-Stoltz-Gaillard.pdf).

Sent April 29:

This week we’ll be reading the paper  “Sparse inverse covariance estimation with the graphical lasso”   by Friedman et al. (http://www-stat.stanford.edu/~tibs/ftp/graph.pdf). The paper discusses the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. The connection to  graphs- conditional independence may be deduced when there is a zero in the inverse covariance matrix; for a reminder on the subject see the attached tutorial.

Causality

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This week will be about Causality. Please read –

– http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.2460 : an article, “causal feature selection”, where we are interested in the causal part: section 1 to 2.2 included, sections 5 and 7. (add section 8 if you want a lengthy list of applications)

– A part of a tutorial presentation on causality. I removed unnecessary slides to make sure you would not print the whole things if you don’t want to. The video and the full slides are available on video lecture, but not necessary for the reading group. However, the guy who gave the tutorial is really good. http://videolectures.net/ecmlpkdd2011_tsamardinos_discovery/

Probabilistic Graphical models – Intro

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Last week we discussed the subject of Probabilistic Graphical Models – an introduction.
It is a good and general introduction which covers the basis, and tries to provide intuitions rather than a perfect mathematical formalism. It is slightly outdated, but it makes many links with other areas of machine learning.