Monday, September 20, 2010

MLSP 2010 highlights

Here are my highlights from MLSP.

To my knowledge this was the first machine learning conference to occur within the arctic circle ~ 68 N. The conference took place on top of the gondola. The key highlight of the conference was the summer bobsled track from the conference center to the village. The food was mostly raindeer (in various forms) and berries ;)

On the technical side:

Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process
Regression Models:
Simo Sarka had a poster where he converted (almost arbitrary) stationary GP time series models into a state space model. He then used to Kalman filter to do O(T) predictions. As opposed to O(T^3) for general GPs and O(T^2) or O(TlogT) with Toeplitz tricks if the time series is in discrete time. Simo's method works in continuous time as well.

Recent directions in nonparametric Bayesian machine learning Zoubin gave a lecture were he made an unapologetic advertisement for NP-Bayes.

Tom M. Mitchell: Machine Learning for Studying Neural Representations of Word Meanings An interesting talk showing the cutting edge of machine learning applied to fMRI data.

PASS-GP: Predictive Active Set Selection for Gaussian Processes A new approach to sparse GPs involving selecting a subset of data points.

Archetypal Analysis for Machine Learning Mikkel's old NIPS pal an enthusiastic talk on "Archetypal Analysis", which most of the MLSP crowd was unfamiliar with.

CBMS highlights

Here are my highlights from CBMS: the non-parametric Bayes conference at UC Santa Cruz. It was organized more like a summer school, however.

The conference was dominated by Peter Muller, who gave 10 1.5 hour lectures on non-parametric Bayes. He talked mainly of Dirichlet processes and the generalizations to them: Pitman-Yor, Polya trees, ect. He presented a "graphical model of graphical models" demonstrating the connection between the related models. He went through each model and compared them by their predictive probability function (PPF), which is the one-step-ahead predictive distribution for the models. Notably absent from his unifying view was Gaussian processes.

Michael Jordan gave one lecture where he went through various models various NP Bayes models he has worked with: LDA, IBPs, sticky HMMs, ... He didn't get too technical, but tried to give a high level view of many models motivated by applications such as speaker diarization.

Wes Johnson gave one lecture giving examples of NP Bayes in biology.

Finally, Peter Hoff gave one lecture "Alternative approaches to Bayesian nonparametrics". He gave some examples of how doing Bayesian inference with an unknown Gaussian has a better predictive probability than using a DP-mixture for N <> 100 were referred to "large" and N > 5000 as "huge".

The slides are available here: