Adversarial Corruption

Adversarial corruption studies the impact of malicious data manipulation on machine learning models, aiming to develop robust algorithms that maintain accuracy despite such attacks. Current research focuses on developing corruption-tolerant algorithms for various models, including gradient descent, maximum likelihood estimation, and contextual bandits, often employing techniques like robust regression, mirror descent, and weighted averaging to mitigate the effects of corrupted data. This field is crucial for enhancing the reliability and security of machine learning systems across diverse applications, from healthcare and finance to autonomous systems, where data integrity is paramount.