Introduction and Exemplars of Uncertainty Decomposition
It provides an introductory overview for researchers or practitioners interested in uncertainty in high-stakes applications like medical diagnosis, but it is incremental as it summarizes existing methods.
The report introduces uncertainty decomposition by explaining two types of uncertainty and providing exemplars such as maximum likelihood estimation and deep neural networks, without presenting new results or numbers.
Uncertainty plays a crucial role in the machine learning field. Both model trustworthiness and performance require the understanding of uncertainty, especially for models used in high-stake applications where errors can cause cataclysmic consequences, such as medical diagnosis and autonomous driving. Accordingly, uncertainty decomposition and quantification have attracted more and more attention in recent years. This short report aims to demystify the notion of uncertainty decomposition through an introduction to two types of uncertainty and several decomposition exemplars, including maximum likelihood estimation, Gaussian processes, deep neural network, and ensemble learning. In the end, cross connections to other topics in this seminar and two conclusions are provided.