Simultaneous Missing Value Imputation and Structure Learning with Groups
This addresses the challenge of discovering learning pathways in education from incomplete student performance data, representing an incremental advance by combining structure learning and imputation in a scalable deep learning framework.
The paper tackles the problem of learning structures between groups of variables from data with missing values, proposing VISL, a scalable approach that simultaneously infers structures and performs imputations using deep learning, showing improved performance on imputation and structure learning accuracy in experiments on synthetic and real-world education datasets.
Learning structures between groups of variables from data with missing values is an important task in the real world, yet difficult to solve. One typical scenario is discovering the structure among topics in the education domain to identify learning pathways. Here, the observations are student performances for questions under each topic which contain missing values. However, most existing methods focus on learning structures between a few individual variables from the complete data. In this work, we propose VISL, a novel scalable structure learning approach that can simultaneously infer structures between groups of variables under missing data and perform missing value imputations with deep learning. Particularly, we propose a generative model with a structured latent space and a graph neural network-based architecture, scaling to a large number of variables. Empirically, we conduct extensive experiments on synthetic, semi-synthetic, and real-world education data sets. We show improved performances on both imputation and structure learning accuracy compared to popular and recent approaches.