Workshop on Data-Efficient Machine Learning (DeMaL)

The recent increase in the size of neural networks has led to a proportional increase in the demands for high-quality human-annotated data. Labeling data is a costly and time-consuming endeavor, and the need for large data is often satiated through creative techniques such as data augmentation, transfer learning, self-supervised learning, active learning, to name a few. Many of these techniques are designed for specific data types such as images, text, and speech. The data in many data-mining applications however is multi-modal in nature, has implicit signals from user-interactions, and involves multiple agents. Given the uniqueness, importance, and growing interest in these problems, we feel that the ACM Conference on Knowledge Discovery and Data Mining (SIGKDD) 2021 is an appropriate venue for running a workshop on Data-efficient Machine Learning. In this proposal, we discuss our vision for this workshop.