BIGDATA: F: DKA: CSD: Human and Machine Co-Processing

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Human experts are crucial to data analysis. Their roles include sifting through large datasets to facilitate search, retrieval, and machine learning. Humans often perform much better than machines at such tasks, but the speed and capacity of human experts is a limiting factor in the human-machine co-processing. This project is addressing two aspects of human-machine co-processing: winnowing Big Data to produce manageable subsets for human expert analysis, and machine learning algorithms that learn efficiently from human experts with a minimal amount of human interaction. This has a wide range of applications; to ensure broad applicability of the results the project is evaluating the techniques in multiple domains: cognitive science, large-scale astronomical data analysis, and experimental design in materials science.The approach used for data winnowing is based on developing predictive models and identifying data that does not fit the models. A key research challenge is non-stationary environments: the underlying model changes over time. Preliminary work shows promise on selection from a finite set of models, and new work investigates more flexible parametric models. The active learning task uses the multi-armed bandit problem to model identify which features have the greatest impact on human decisions. This task also investigates learning from comparisons/rankings rather than predictions; conjecturing that there may exist low-dimensional structure governing human reasoning and decision-making that enables learning with significantly fewer comparisons than might otherwise be required. A common theme in both tasks is ensuring computational complexity is low enough to facilitate real-time interactions with human experts in spite of the volume of data. This is achieved using bounded approximations and convex relaxations of the optimization programs used to guide the interaction.