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

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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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