
MLSS Tutorials
April 25 is a joint tutorial day of AISTATS and the MLSS Machine Learning Summer School.
Tutorial Speakers

Roderick MurraySmith,
University of Glasgow
Machine learning and Human Computer Interaction
The opportunities for interaction with computer systems are rapidly expanding beyond traditional input and
output paradigms: fullbody motion sensors, braincomputer interfaces, 3D displays, touch panels are now
commonplace commercial items. The profusion of new sensing devices for human input and the new display channels
which are becoming available offer the potential to create more involving, expressive and efficient interactions
in a much wider range of contexts. Dealing with these complex sources of human intention requires appropriate
mathematical methods; modelling and analysis of interactions requires sophisticated methods which can transform
streams of data from complex sensors into estimates of human intention.
This tutorial will focus on the use of inference and dynamical modelling in humancomputer interaction. The
combination of modern statistical inference and realtime closed loop modelling offers rich possibilities in
building interactive systems, but there is a significant gap between the techniques commonly used in HCI and the
mathematical tools available in other fields of computing science. This tutorial aims to illustrate how to bring
these mathematical tools to bear on interaction problems, and will cover basic theory and example applications
from mobile interaction, interaction with large music collections and fullbody interaction.


Christian P. Robert,
Ceremade  Université ParisDauphine
Approximate Bayesian computation (ABC), methodology and
applications
ABC appeared in 1999 to solve complex genetic problems where the likelihood of the model was impossible
to compute. They are now a standard tool in the statistical genetic community but have also addressed many
other problems where likelihood computation was also an issue, including dynamic models in signal
processing and financial data analysis. However, these methods suffer to some degree from calibration
difficulties that make them rather volatile in their implementation and thus render them suspicious to the
users of more traditional Monte Carlo methods. Nonetheless, ABC techniques have several claims to
validity: first, they are connected with econometric methods like indirect inference. Second, they can be
expressed in terms of various nonparametric estimators of the likelihood or of the posterior density and
follow standard convergence patterns. At last, they appear as regular Bayesian inference over noisy data.
The tutorial covers those validation steps but also details different implementations of ABC algorithms
and calibration of their parameters.


Håvard Rue,
Norwegian University of Science and Technology
Bayesian computing with INLA
In this lecture, I will discuss approximate Bayesian inference for the
class of latent Gaussian models (LGMs). LGMs are perhaps the most
commonly used class of models in statistical applications. It includes,
among others, most of (generalised) linear models, (generalised)
additive models, smoothing spline models, state space models,
semiparametric regression, spatial and spatiotemporal models,
logGaussian Cox processes and geostatistical and geoadditive models.
The concept of LGMs is extremely useful when doing inference as we can
treat models listed above in a unified way and using the same algorithms
and software tool. Our approach to (approximate) Bayesian inference, is
to use integrated nested Laplace approximations (INLA). Using this new
tool, we can directly compute very accurate approximations to the
posterior marginals. Another advantage with our approach is its
generality, which makes it possible to perform Bayesian analysis in an
automatic, streamlined way, and to compute model comparison criteria and
various predictive measures so that models can be compared and the model
under study can be challenged.
I will discuss the background for understanding LGM and INLA, end by
illustrating INLA on some examples in R. Please visit
www.rinla.org to
download the package and for further information.



