The Probabilistic Learning Research Group at Biointelligence Lab investigates machine learning algorithms for
probabilistic graphical models such as hierarchical Bayesian networks (HBN) and self-organizing latent lattice models (SOLL).
Our current research
focuses on structural learning of large-scale probabilistic graphical
models in a noisy and/or dynamic environment. For example, we are developing methods for learning large-scale Bayesian networks (having more than 10,000 nodes) from sparse datasets. Major application areas of these techniques include text mining and multi-modal associative information analysis.
ICML 2012, The 29th International Conference on Machine Learning, June 26 ~ July 1, 2012, Edinburgh, Scotland.
UAI 2012, The 28th Conference on Uncertainty in Artificial Intelligence, August 15 ~ 17, 2012, Catalina Island, California, USA.
ECML/PKDD 2012, The 23rd European Conference on Machine Learning / The 16th Practice of Knowledge Discovery in Databases, Sep. 24 ~ 28, 2012, Bristol, UK.
NIPS 2012, The 26th Conference on
Neural Information Processing Systems, 3 ~ 8, 2012, Lake Tahoe, Nevada, United States.