title: ÐÐ°ÑÑÑÐ°Ð±Ð¸ÑÑÐµÐ¼ÑÐ¹ Ð¿Ð¾Ð¸ÑÐº Ð·Ð½Ð°ÑÐ¸Ð¼ÑÑ ÑÑÐµÐ·Ð¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¾ÑÐ»Ð°Ð´ÐºÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Large-Scale Slice Finding for ML Model Debugging
Creators: name: Ð¡Ð°Ð³Ð°Ð´ÐµÐµÐ²Ð°, Ð¡Ð²ÐµÑÐ»Ð°Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÑÑÐµÐ·Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð¾ÑÐ»Ð°Ð´ÐºÐ° Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
k-slicing
machine learning models
data analysis

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description: Recent work on debugging machine learning (ML) models aims to find the top-k data slices (e.g., conjunctions of predicates such as âgender = femaleâ and âdegree = PhDâ), where a trained model performs significantly worse than on the entire training/test data. These slices may be used to
evaluate informative metrics for the problematic subsets, add rules, or otherwise improve the model.
The general slice finding problem allows for overlapping slices, which is important for high quality analysis covering non-individual combinations of features and their distinct values. Existing works
primarily rely on heuristics and focus on small datasets that fit in memory of a single node. We address these scalability limitations of slice finding in a holistic manner from both algorithmic and systems perspectives. Upper and lower bounds are established regarding slice sizes, errors, and objectives to facilitate effective pruning as well as alternative slice enumeration strategies. Additionally, we present local and distributed operators in order to provide processing of big datasets. 
Experiments over datasets show that effective pruning renders exact enumeration feasible, even for datasets with thousands of features.