Poster
in
Workshop: Structured Probabilistic Inference and Generative Modeling
Regularized Data Programming with Automated Bayesian Prior Selection
Jacqueline Maasch · Hao Zhang · Qian Yang · Fei Wang · Volodymyr Kuleshov
Keywords: [ automated prior selection ] [ Bayesian priors ] [ automated data labeling ] [ data programming ] [ literature mining ] [ information extraction ]
The cost of manual data labeling can be a significant obstacle in supervised learning. Data programming (DP) offers a weakly supervised solution for training dataset creation, wherein the outputs of user-defined programmatic labeling functions (LFs) are reconciled through unsupervised learning. However, DP can fail to outperform an unweighted majority vote in some scenarios, including low-data contexts. This work introduces a Bayesian extension of classical DP that mitigates failures of unsupervised learning by augmenting the DP objective with regularization terms. Regularized learning is achieved through maximum a posteriori estimation in the Bayesian model. Majority vote is proposed as a proxy signal for automated prior parameter selection. Results suggest that regularized DP improves performance relative to maximum likelihood and majority voting, confers greater interpretability, and bolsters performance in low-data regimes.