[Lisa_seminaires] Fwd: [Lisa_labo] Talk by Anoop Korattikara: Thursday, June 6th at 2:00pm (AA3195)

Yoshua Bengio bengioy at iro.umontreal.ca
Jeu 6 Juin 07:06:46 EDT 2013


Reminder:

Begin forwarded message:

> From: Guillaume Desjardins <guillaume.desjardins at gmail.com>
> Subject: [Lisa_labo] Talk by Anoop Korattikara: Thursday, June 6th at 2:00pm (AA3195)
> Date: 3 juin 2013 09:24:01 HAE
> To: lisa_seminaires at iro.umontreal.ca, lisa_teatalk at iro.umontreal.ca
> Cc: kb.anoop at gmail.com, lisa LABO <lisa_labo at iro.umontreal.ca>
> 
> Please join us this upcoming Thursday (June 6th) for the following
> talk given by Anoop Korattikara (student of Max Welling). The talk
> will be held at 2:00pm in AA3195 as usual.
> 
> Title: Markov Chain Monte Carlo and the Bias-Variance Tradeoff
> 
> Bayesian posterior sampling can be painfully slow on very large
> datasets, since traditional MCMC methods such as Hybrid Monte Carlo
> are designed to be asymptotically unbiased and require processing the
> entire dataset to generate each sample. Thus, given a small amount of
> sampling time, the variance of estimates computed using such methods
> could be prohibitive. We argue that lower risk estimates can often be
> obtained using ™approximate™ MCMC methods that mix very fast (and thus
> lower the variance quickly) at the expense of a small bias in the
> stationary distribution. I will first talk about two such biased
> algorithms: Stochastic Gradient Langevin Dynamics and its successor
> Stochastic Gradient Fisher Scoring, both of which use stochastic
> gradients estimated from mini-batches of data, allowing them to mix
> very fast. Then I will present our current work on a new (biased) MCMC
> algorithm that uses a sequential hypothesis test to approximate the
> Metropolis-Hastings test, allowing us to accept/reject samples with
> high confidence using only a fraction of the data required for the
> exact test.
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