Natural robustness of machine learning in the open world - Wei, Hongxin (Nanyang Technological University, 2023)
Modern machine learning techniques have demonstrated their excellent capabilities in many areas. Despite the human-surpassing performance in experimental settings, many researches have revealed the vulnerability of machine learning models caused by the violation of fundamental assumptions in real-world applications. Such issues significantly hinder the applicability and reliability of machine learning. This motivates the need to preserve model performance under naturally induced data corruptions or alterations across the machine learning pipeline, which is termed ``Natural Robustness". To this end, this thesis starts by investigating two naturally occurring issues: label corruption and distribution shift. Thereafter, we proceed by exploring the value of out-of-distribution data in the robustness of machine learning.