The availability of large annotated data can be a critical bottleneck in
training machine learning algorithms successfully, especially when applied to
diverse domains. Weak supervision offers a promising alternative by
accelerating the creation of labeled training data using domain-specific rules.
However, it requires users to write a diverse set of high-quality rules to
assign labels to the unlabeled data. Automatic Rule Induction (ARI) approaches
circumvent this problem by automatically creating rules from features on a
small labeled set and filtering a final set of rules from them. In the ARI
approach, the crucial step is to filter out a set of a high-quality useful
subset of rules from the large set of automatically created rules. In this
paper, we propose an algorithm (Filtering of Automatically Induced Rules) to
filter rules from a large number of automatically induced rules using
submodular objective functions that account for the collective precision,
coverage, and conflicts of the rule set. We experiment with three ARI
approaches and five text classification datasets to validate the superior
performance of our algorithm with respect to several semi-supervised label
aggregation approaches. Further, we show that achieves statistically
significant results in comparison to existing rule-filtering approaches.

自动过滤通过次模的目标函数由大量自动生成的规则组成的规则集，该算法在弱监督下实现了更优越的性能，并且相对现有的规则过滤方法取得了显著的结果。

FAIR：自动诱导规则的过滤

FAIR: Filtering of Automatically Induced Rules

We propose to automatically create capsule wardrobes. Given an inventory of
candidate garments and accessories, the algorithm must assemble a minimal set
of items that provides maximal mix-and-match outfits. We pose the task as a
subset selection problem. To permit efficient subset selection over the space
of all outfit combinations, we develop submodular objective functions capturing
the key ingredients of visual compatibility, versatility, and user-specific
preference. Since adding garments to a capsule only expands its possible
outfits, we devise an iterative approach to allow near-optimal submodular
function maximization. Finally, we present an unsupervised approach to learn
visual compatibility from "in the wild" full body outfit photos; the
compatibility metric translates well to cleaner catalog photos and improves
over existing methods. Our results on thousands of pieces from popular fashion
websites show that automatic capsule creation has potential to mimic skilled
fashionistas in assembling flexible wardrobes, while being significantly more
scalable.

本文提出采用子集选择算法以及亚模旋函数捕捉视觉兼容性、服装多功能性和用户特殊偏好的关键因素等方式自动创建多样混搭的胶囊衣柜，同时利用无监督学习从实际照片中学习视觉兼容性。其结果表明胶囊衣柜自动化的创建方式可以在较大规模上模拟专业时尚达人的思维和效率。