Teammates for Project

[Manzil Zaheer]

I am considering to work small-sample size problems, wherein we have to learn/estimate parameters of the underlying distribution from very small number of examples for each population. Such problems arise in multiple practical scenarios like performance analysis of a new VLSI technology, predicting player performance in a season from a few early games, UBM models for speaker recognition etc.

I am planning to work on the line of James-Stein Estimator (which is always better than MLE for 3 or higher dimension problems) and exploit the correlation existing between each population.

I am a first year PhD student at ECE. If anyone is interested in this idea or has some similar ideas please reply.

Thanking you,

Manzil

[Alex Smola]

hi manzil,

my 2c on this - yes, the james stein estimator is better than mle. that's in fact a famous example. however, you're still throwing away way too much information if you use this rather than designing proper priors or alternatively approaching things with a very tightly controlled hypothesis class. i'm talking constants and not rates. if rates are all you want, this is relatively easy. 

go for it if you're very good with proofs and statistical analysis. otherwise you'll find it difficult.

cheers,

alex

--
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http://www.youtube.com/playlist?list=PLZSO_6-bSqHQmMKwWVvYwKreGu4b4kMU9 (YouTube playlist)
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