Poster
in
Workshop: Machine Learning for Data: Automated Creation, Privacy, Bias
Bayesian Regression from Multiple Sources of Weak Supervision
Putra Manggala · Holger Hoos · Eric Nalisnick · Putra Manggala
Abstract:
We describe a Bayesian approach to weakly supervised regression. Our proposed framework propagates uncertainty from the weak supervision to an aggregated predictive distribution. We use a generalized Bayes procedure to account for the supervision being weak and therefore likely misspecified.
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