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.

Elastic net, LASSO, and LARS in Python

5 years ago