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The Mondrian Process for Machine Learning
Matej Balog, Yee Whye Teh
This report is concerned with the Mondrian process and its applications in machine learning. The Mondrian process is a guillotine-partition-valued stochastic process that possesses…
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive per…
Particle Gibbs for Bayesian Additive Regression Trees
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, w…