  Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to
synthesize training labels efficiently. The core component of PWS is the label
model, which infers true labels by aggregating the outputs of multiple noisy
supervision sources abstracted as labeling functions (LFs). Existing
statistical label models typically rely only on the outputs of LF, ignoring the
instance features when modeling the underlying generative process. In this
paper, we attempt to incorporate the instance features into a statistical label
model via the proposed FABLE. In particular, it is built on a mixture of
Bayesian label models, each corresponding to a global pattern of correlation,
and the coefficients of the mixture components are predicted by a Gaussian
Process classifier based on instance features. We adopt an auxiliary
variable-based variational inference algorithm to tackle the non-conjugate
issue between the Gaussian Process and Bayesian label models. Extensive
empirical comparison on eleven benchmark datasets sees FABLE achieving the
highest averaged performance across nine baselines.
