ALabel model and illustrations

A.1 Majority Voting The Majority Voting (MV) is the most intuitive algorithm for aggregate LFs' annotations. We omit this case for simplicity. A.3 Snorkel MeTaL The parameters µof Snorkel MeTaL [31] are given by Bayes' theorem we have: pµ(y = c,λ = m) = pµ(λ = m | y = c)p(y = c) = Consider a label model g(L(x),x) F in arbitrary functional class F, e.g., neural network, and having additional dependency on data feature x4, we can still approximate such complicated function with identity function-based label model g W(x)(L(x)) similar to the aforementioned one except that W(x): X RM (C+1) C is a similarly complicated function, e.g., neural network, that maps each data x X to a unique label model parameter W(x). We leave the exploration of more complicated form of label models into future work. B.1 Case 1: identity function We define the loss with reweighted sample as, Instead of employing the decomposing loss function, we introduce a more general influence estimation method - weight-moving Influence, which get ride of the loss decomposition and approximation and is agnostic to the selection of σ() function.