We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statisticallysignificant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks.
Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. First, existing tools require well-trained data scientists to select the appropriate techniques to build models and to evaluate their outcomes. Second, existing tools require heavy data preparation steps and are often too slow to give interactive feedback to domain experts in the model building process, severely limiting the possible interactions. Third, current tools do not provide adequate analysis of statistical risk factors in the model development. In this work, we present the first iteration of QuIC-M (pronounced quick-m), an interactive humanin-the-loop data exploration and model building suite. The goal is to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions. ACM Reference Format:
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