The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

The diffusion model has shown remarkable performance in modeling data distributions and
synthesizing data. However, the vanilla diffusion model requires complete or fully observed …

The issue of missing data in supervised learning has been largely overlooked, especially in
the deep learning community. We investigate strategies to adapt neural architectures for …

Complete and accurate data is an important enabler of effective supply chain decision
making. Despite the increasing efforts to fully automate data collection processes using …

Missing values in real-world data pose a significant and unique challenge to algorithmic
fairness. Different demographic groups may be unequally affected by missing data, and the …

The virtual water (VW) approach offers a crucial heuristic tool to analyze water and food
security by considering the water embedded in grain during the whole production process …

Missing data is common in applied data science, particularly for tabular data sets found in
healthcare, social sciences, and natural sciences. Most supervised learning methods only …

Energy forecasting models deployed in industrial applications face uncertainty wrt data
availability, due to network latency, equipment malfunctions or data-integrity attacks. In …

Constant (naive) imputation is still widely used in practice as this is a first easy-to-use
technique to deal with missing data. Yet, this simple method could be expected to induce a …

Missing values are unavoidable in many applications of machine learning and present
challenges both during training and at test time. When variables are missing in recurring …