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Bayesian Regression from Multiple Sources of Weak Supervision
Putra Manggala · Holger Hoos · Eric Nalisnick · Putra Manggala

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.

Author Information

Putra Manggala (Shopify)
Holger Hoos (Leiden Institute of Advanced Computer Science, Leiden University)
Eric Nalisnick (University of Amsterdam)
Putra Manggala (University of Amsterdam)

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