Here is a list of all my research publications or work.
Please report me any broken link, etc. Also feel free to contact me about anything related to these publications.
In machine-learning, Markov Chain Monte Carlo (MCMC) strategies such as Gibbs sampling are important
approximate inference techniques. They use a Markov Chain mechanism to explore and sample the state
space of a target distribution. The generated samples are then used to approximate the target
distribution.
MCMC is mathematically guaranteed to converge with enough samples. Yet some complex graphical models
can cause it to converge very slowly to the true distribution of interest. Improving the quality
and efficiency of MCMC methods is an active topic of research in the probabilistic graphical
models field. One possible method is to “block” some parts of the graph together, sampling groups
of variables instead of single variables.
In this document, we concentrate on a particular blocking scheme known as tree sampling. Tree
sampling operates on groups of trees, and as such requires that the graph be partitioned
in a special way prior to inference. We present new algorithms to find tree partitions on
arbitrary graphs. This allows tree sampling to be used on any undirected probabilistic graphical model.
This is my thesis for the completion of my Msc. degree at UBC. It contains almost all my research
results in inference algorithms for undirected graphical models.
(10/10/2005)
This is a research report in History, with the 1917-1941 period in Russia as a background.
This work investigates how repression and falsification, two of the methods used by the soviet
regime to remain in power, were described in a popular form of the traditional Russian folklore,
the "Tchastouchki".
This work was completed at
Université Paris IV - Sorbonne
, under the supervision of Professor
Francis Conte. It was a requirement for the completion of my degree at
Ecole Polytechnique.
Warning:
This report is in French, with some quotes in Russian.
(01/07/2003)