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.

The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately. The first algorithm, called Divisive K-means, improves the speed of IKM by speeding up its splitting process of clusters. Testing with UCI Machine Learning data sets, the new algorithm achieves the empirically global optimum as IKM and has lower complexity, $O(k*log_{2}k*n)$, than IKM, $O(k^{2}n)$. The second algorithm, called Parallel Two-Phase K-means (Par2PK-means), parallelizes IKM by employing the model of Two-Phase K-means. Testing with large data sets, this algorithm attains a good speedup ratio, closing to the linearly speed-up ratio.