Jialin Lu

Ali Arab, Dev Arora, Jialin Lu et al.

Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstructured, high-dimensional data such as images. This is because subgroup discovery algorithms rely on defining descriptive rules based on (attribute, value) pairs, however, in unstructured data, an attribute is not well defined. Even in cases where the notion of attribute intuitively exists in the data, such as a pixel in an image, due to the high dimensionality of the data, these attributes are not informative enough to be used in a rule. In this paper, we introduce the subgroup-aware variational autoencoder, a novel variational autoencoder that learns a representation of unstructured data which leads to subgroups with higher quality. Our experimental results demonstrate the effectiveness of the method at learning subgroups with high quality while supporting the interpretability of the concepts.

Zihan Wang, Jialin Lu, Oliver Snow et al.

Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency. The ability to interpret the predictions made by machine learning models and accurately evaluate these models is crucially important. In this paper, we present an interactive visualization tool to elucidate the training process of active learning. This tool enables one to select a sample of interesting data points, view how their prediction values change at different querying stages, and thus better understand when and how active learning works. Additionally, users can utilize this tool to compare different active learning strategies simultaneously and inspect why some strategies outperform others in certain contexts. With some preliminary experiments, we demonstrate that our visualization panel has a great potential to be used in various active learning experiments and help users evaluate their models appropriately.