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In the domains of dataset construction and crowdsourcing, a notable challenge is to aggregate labels from a heterogeneous set of labelers, each of whom is potentially an expert in some subset of tasks (and less reliable in others). To reduce costs of hiring human labelers or training automated labeling systems, it is of interest to minimize the number of labelers while ensuring the reliability of the resulting dataset. We model this as the problem of performing K-class classification using the predictions of smaller classifiers, each trained on a subset of [K], and derive bounds on the number of classifiers needed to accurately infer the true class of an unlabeled sample under both adversarial and stochastic assumptions. By exploiting a connection to the classical set cover problem, we produce a near-optimal scheme for designing such configurations of classifiers which recovers the well known one-vs.-one classification approach as a special case. Experiments with the MNIST and CIFAR-10 datasets demonstrate the favorable accuracy (compared to a centralized classifier) of our aggregation scheme applied to classifiers trained on subsets of the data. These results suggest a new way to automatically label data or adapt an existing set of local classifiers to larger-scale multiclass problems. 

Augmented reality (AR) technologies are rapidly gaining momentum in society and are expected to play a critical role in the future of cities and transportation. In such dynamic settings with a heterogeneous population of AR users, it is important for holograms to be placed in the surrounding environment with regard to the users' preferences. However, the area of AR personalization remains largely unexplored. This paper proposes to use behavioral cloning, an algorithm for imitation learning, as a means of automatically generating policies that capture user preferences of hologram positioning. We argue in favor of employing the fog computing paradigm to minimize the volume of data sent to the cloud, and thereby preserve user privacy and increase both communication efficiency and learning efficiency. Through preliminary results obtained with a custom, Unity-based AR simulator, we demonstrate that user-specific policies can be learned quickly and accurately.