Imputation Task

Imputation addresses the pervasive problem of missing data in datasets, aiming to accurately estimate missing values to enable reliable data analysis and model training. Current research focuses on developing sophisticated imputation methods using diverse model architectures, including generative adversarial networks (GANs), transformers, large language models (LLMs), and diffusion models, often tailored to specific data types (e.g., time series, tabular data, graphs). These advancements improve the accuracy and robustness of imputation, particularly in complex scenarios with various missingness mechanisms, leading to more reliable results in downstream tasks across numerous fields, from healthcare and finance to recommendation systems and anomaly detection.