Missingness Resilient

Missingness-resilient methods aim to develop robust machine learning models that can effectively handle incomplete data, a pervasive issue across diverse scientific domains. Current research focuses on developing algorithms and model architectures that either incorporate missingness information directly into the model or leverage auxiliary data to improve prediction accuracy despite incomplete observations, employing techniques like imputation, data fusion, and specialized model designs (e.g., modified U-Nets for diffusion models). This work is crucial for improving the reliability and generalizability of machine learning models in real-world applications where complete data is rarely available, impacting fields from healthcare and speech recognition to computer vision and causal inference.