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A common problem practitioners face is to select rare events in a large dataset. Unfortunately, standard techniques ranging from pre-trained models to active learning do not leverage proximity structure present in many datasets and can lead to worse-than-random results. To address this, we propose EZMODE, an algorithm for iterative selection of rare events in large, unlabeled datasets. EZMODE leverages active learning to iteratively train classifiers, but chooses the easiest positive examples to label in contrast to standard uncertainty techniques. EZMODE also leverages proximity structure (e.g., temporal sampling) to find difficult positive examples. We show that EZMODE can outperform baselines by up to 130× on a novel, real-world, 9,000 GB video dataset. 

Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML. In this paper, we present WILLUMP, an optimizer for ML inference that introduces two statistically-motivated optimizations targeting ML applications whose performance bottleneck is feature computation. First, WILLUMP automatically cascades feature computation for classification queries: WILLUMP classifies most data inputs using only high-value, low-cost features selected through empirical observations of ML model performance, improving query performance by up to 5× without statistically significant accuracy loss. Second, WILLUMP accurately approximates ML top-K queries, discarding low-scoring inputs with an automatically constructed approximate model and then ranking the remainder with a more powerful model, improving query performance by up to 10× with minimal accuracy loss. WILLUMP automatically tunes these optimizations’ parameters to maximize query performance while meeting an accuracy target. Moreover, WILLUMP complements these statistical optimizations with compiler optimizations to automatically generate fast inference code for ML applications. We show that WILLUMP improves the end-to-end performance of real-world ML inference pipelines curated from major data science competitions by up to 16× without statistically significant loss of accuracy.