Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. However, data programming relies on designing labeling functions which still requires significant domain expertise. Also, it is prohibitively difficult to write labeling functions for image datasets as it is hard to express domain knowledge using raw features for images (pixels). We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. The core premise of affinity coding is that the affinity scores of instance pairs belonging to the same class on average should be higher than those of pairs belonging to different classes, according to some affinity functions. We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set. We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a minimum of 71% to a maximum of 98% without requiring any extensive human annotation. In terms of end-to-end performance, GOGGLES outperforms the state-of-the-art data programming system Snuba by 21% and a state-of-the-art few-shot learning technique by 5%, and is only 7% away from the fully supervised upper bound.

Entity resolution (ER) refers to the problem of matching records in one or more relations that refer to the same real-world entity. While supervised machine learning (ML) approaches achieve the state-of-the-art results, they require a large amount of labeled examples that are expensive to obtain and often times infeasible. We investigate an important problem that vexes practitioners: is it possible to design an effective algorithm for ER that requires Zero labeled examples, yet can achieve performance comparable to supervised approaches? In this paper, we answer in the affirmative through our proposed approach dubbed ZeroER. Our approach is based on a simple observation --- the similarity vectors for matches should look different from that of unmatches. Operationalizing this insight requires a number of technical innovations. First, we propose a simple yet powerful generative model based on Gaussian Mixture Models for learning the match and unmatch distributions. Second, we propose an adaptive regularization technique customized for ER that ameliorates the issue of feature overfitting. Finally, we incorporate the transitivity property into the generative model in a novel way resulting in improved accuracy. On five benchmark ER datasets, we show that ZeroER greatly outperforms existing unsupervised approaches and achieves comparable performance to supervised approaches.

In Android malware detection, recent work has shown that using contextual information of sensitive API invocation in the modeling of applications is able to improve the classification accuracy. However, the improvement brought by this context-awareness varies depending on how this information is used in the modeling. In this paper, we perform a comprehensive study on the effectiveness of using the contextual information in prior state-of-the-art detection systems. We find that this information has been "over-used" such that a large amount of non-essential metadata built into the models weakens the generalizability and longevity of the model, thus finally affects the detection accuracy. On the other hand, we find that the entrypoint of API invocation has the strongest impact on the classification correctness, which can further improve the accuracy if being properly captured. Based on this finding, we design and implement a lightweight, circumstance-aware detection system, named "PIKADROID" that only uses the API invocation and its entrypoint in the modeling. For extracting the meaningful entrypoints, PIKADROID applies a set of static analysis techniques to extract and sanitize the reachable entrypoints of a sensitive API, then constructs a frequency model for classification decision. In the evaluation, we show that this slim model significantly improves the detection accuracy on a data set of 23,631 applications by achieving an f-score of 97.41%, while maintaining a false positive rating of 0.96%.