Probabilistic Learning Research Group at BI Lab

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.

Upcoming Conferences on Probabilistic Graphical Models

• 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.
  

Seminars

• Bayesian Time Series Models
  (July, 2012)
• 'Pattern Recognition and Machine Learning' Seminar
  (1st: Dec. 2006 ~ Feb. 2007, 2nd: Mar. 2007~Jul. 2007)
• Hypernetwork Models of Learning and Memory
  (Sep. ~ Dec. 2006)
• Bayesian Networks Seminar
  (July ~ Aug. 2003)
• Adaptive Sampling Techniques for Large-scale Probabilistic Graphical Models
  (Sep. ~ Nov. 2002)
• Latent Variable Model Seminar
  (Feb. 2001)
• Bayesian Network Group Seminar 2000
  (Oct. ~ Nov. 2000)
• Seminar on Probabilistic Graphical Models
  (July ~ Aug. 2000)
• Graphical Models Seminar
  (Oct. 1999)

Invited Talks

Tutorials

• Zoubin Ghahramani, Bayesian Modelling, Graphical Models, and Semi-supervised Learning, 2012 Machine Learning Summer School.
• Hinton, G., Recent Developments in Deep Learning, Google Tech Talk, 2010.
• Hinton, G., Deep Belief Nets, NIPS 2007 Tutorial, 2007.
• Heckerman, D., Graphical Models for Data Mining, KDD 2004 Invited Talk, 2004.
• Zhang, B.-T., Machine Learning for Biological Data Mining, A tutorial presented at ETRI, Mar. 6, 2001.
• Friedman, N. and Koller, D., Learning Bayesian Networks From Data, NIPS 2001 Tutorial, 2001.
• Heckerman, D., A Tutorial on Learning With Bayesian Networks, Technical Report MSR-TR-95-06, Microsoft Research, 1996.
• Dayan, P., Hinton, G.E., Neal, R.M., and Zemel, R.S., The Helmholtz Machine, Neural Computation, 7, 889-904, 1995.

Research Projects

• Videome, Cognitive Machine Learning from Digital Videos
• MARS: A Multimodal Associative Recommendation System
• DNAChipBench (NRL Project): Intelligent Design and Analysis Technology for DNA Chip
• BrainGene: DNA data mining for the analysis of expression patterns of vertebrate brain development-specific genes (rat)
• LaText: Text mining based on latent variable models
• MrHumor: A personalized Internet agent that recommends humors and jokes

Publications

Useful links

• Other Research Groups for Probabilistic Graphical Models
  • Empirical Inference group in the MPI for Biological Cybernetics
  • Machine Learning Lab in the Hebrew University
  • UCLA Cognitive Systems Laboratory
  • Machine Learning and Perception Group in the Microsoft Research Cambridge
  • Machine Learning Group in the University of Cambridge
  • DAGS (Daphne Koller group) in the Stanford University
  • HUGIN Expert Group
• Journals
  • Machine Learning
  • Journal of Machine Learning Research
  • Foundations and Trends in Machine Learning
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Journal of Computational Biology
• Conferences
  • International Conference on Machine Learning 2012
  • Uncertainty in Artificial Intelligence 2012
  • European Conference on Machine Learning 2012
  • Neural Information Processing Systems 2012
• Resources
• Bayesian Network Softwares
  • GeNIe & SMILE (Structural Modeling, Inference, and Learning Engine)
  • Netica
  • J. Cheng's Bayesian Belief Network Software
  • JavaBayes
  • Microsoft Belief Network Tools (MSBN)
  • Bayesware
  • Bayes Net Toolbox for Matlab
• Machine Learning Summer School
• MLSS Kyoto, Japan, 2012
• MLSS Cambridge, UK, 2009