2006
NIPS
NeurIPS 2006
Bayesian Ensemble Learning
Abstract
We develop a Bayesian "sum-of-trees" model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting and inference are accomplished via an iterative backfitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting, each weak learner (i.e., each weak tree) contributes a small amount to the overall model. However, our procedure is defined by a statistical model: a prior and a likelihood, while boosting is defined by an algorithm. This model-based approach enables a full and accurate assessment of uncertainty in model predictions, while remaining highly competitive in terms of predictive accuracy.
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Conference Pioneer
— NIPS 2006
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Topic Pioneer
— Self-Supervised Learning
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Self-Supervised Learning
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Keyword Pioneer
— bayesian ensemble
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— ensemble learning
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Regression
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Types > Ensemble Learning
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo