Re: [AIRG] Correlation explanation, Nov. 28

[Aubrey Barnard <barnard@xxxxxxxxxxx>]

AIRG,

Welcome back! Today, Lucas will be telling us about learning the 
structure of data by using mutual information to construct a hierarchy 
of explanations. The hierarchical aspect goes beyond standard structure 
learning, so this work brings a new perspective (to me, at least).

4pm, CS *3310*

https://arxiv.org/abs/1406.1222
https://papers.nips.cc/paper/5580-discovering-structure-in-high-dimensional-data-through-correlation-explanation

Aubrey


On 11/23/18 8:49 PM, Lucas Morton via AIRG wrote:
> Is there a principled way to learn the underlying structure of a data 
> source in an unsupervised manner, with minimal prior knowledge? A 
> promising approach appeals to Reichenbach's principle: every correlation 
> deserves an explanation. Formalizing this intuition leads to a general 
> information-theoretic approach to unsupervised learning.
> 
> /Discovering Structure in High-Dimensional Data Through Correlation 
> Explanation/
> Greg Ver Steeg, Aram Galstyan
> NIPS 2014
> https://arxiv.org/abs/1406.1222
> 
> 4pm, Wednesday November 28
> CS 4310
> 
> --Lucas
> --------------------------
> Lucas A. Morton
> Postdoctoral Researcher
> Plasma Spectroscopy for Turbulence and Instabilities Group
> Department of Engineering Physics
> UW - Madison
> 
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