H O M E

Brief Bio: I am a Killam postdoctoral fellow in machine learning working with Kevin Murphy and Nando de Freitas in the department of computer science at the University of British Columbia. I completed my PhD in machine learning in the department of computer science at the University of Toronto in 2008 working under Rich Zemel.

Research Interests: My research interests lie at the intersection of artificial intelligence, machine learning and statistics. I am particularly interested in hierarchical graphical models and approximate inference/learning techniques including Markov Chain Monte Carlo and variational Bayesian methods. I am also interested in the study of non-likelihood-based inductive principles for statistical models and the trade-off between statistical consistency/efficiency and computational efficiency. I am interested in a broad range of applications for these modeling and learning techniques including classification, collaborative filtering, ranking, unsupervised structure discovery, feature induction and object recognition/image labeling.

Recent Papers:

[Dec 11, 2009] Baback Moghaddam, Benjamin M. Marlin, Emtiyaz Khan, Kevin Murphy. Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. Proceedings of the Neural Information Processing Systems Conference (NIPS), 2009. [Paper PDF] [Poster PDF] [Oct 26, 2009] Benjamin M. Marlin and Richard Zemel. Collaborative Prediction and Ranking with Non-Random Missing Data. Proceedings of the 3rd ACM Conference on Recommender Systems. 2009. Winner of best technical paper award. [Paper PDF] [Slides PDF]. [June 20, 2009] Benjamin M. Marlin, Mark Schmidt and Kevin P. Murphy. Group Sparse Priors for Covariance Estimation. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009. [Paper PDF] [Poster PDF] [June 16, 2009] Benjamin M. Marlin and Kevin P. Murphy. Sparse Gaussian Graphical Models with Unknown Block Structure. Proceedings of the 26th International Conference on Machine Learning. 2009. [Paper PDF] [Slides PDF] [Poster PDF]
NIPS 2009	UAI 2009	ICML 2009

PhD Thesis: My PhD thesis is titled Missing Data Problems in Machine Learning. It deals with the problem of unsupervised learning in the presence of non-random missing data, as well as the problem of classification in the presence of missing features. The work on non-random missing data is motivated by the problem of rating prediction in collaborative filtering, and uses a new data set collected at Yahoo! Research with Malcolm Slaney. The work on classification with missing features focuses on medical decision making using standard data sets, as well as higher dimensional tasks like digit classification with missing pixels. [Thesis Abstract] [Thesis PDF] [Short Defense Slides PDF] [Long Defense Slides PDF]

Reading Groups: I maintain the website for the Machine Learning and Computational Neuroscience reading groups at UBC.

Email: bmarlin[at]cs[dot]ubc[dot]ca