General Linear Corruption Process

General linear corruption processes model the degradation of data in machine learning, aiming to understand and mitigate the impact of noisy or manipulated inputs on model performance. Current research focuses on developing robust algorithms, such as trimmed maximum likelihood estimators and novel score matching methods like Soft Score Matching, to handle various corruption types, including label and attribute corruptions, across different model classes (e.g., generalized linear models, diffusion models). This work is crucial for improving the reliability and robustness of machine learning systems in real-world applications where data quality is often compromised. The development of theoretically sound and practically effective methods for handling corrupted data is essential for advancing the field.