AutoML-NAS

AutoML, which aims to automate a machine learning system, has attracted a lot of attention in the research community and made a lot of noise in industry and media. An ML system typically learns from a given dataset D, via optimizing a certain loss L, within a particular hypothesis (function) space F. AutoML covers a wide spectrum of important problems:
1. Data, which aims to find the best training data D and the data processing pipeline for the task at hand. Data plays a similar role to machine learning such as textbooks in human learning.
2. Loss function, which aims to design the most appropriate loss function L to be optimized.
3. Hypothesis space, which aims to identify the hypothesis space F that the model belongs to.

NAS, abbreviation for neural architecture search, is an important topic in AutoML, which aims to automatically design well-performing neural network architectures for specific target task, to explore neural network architectures that outperform the ones designed by human experts, and largely reduce the human efforts. We mainly focus on general NAS algorithms/methods, and the applications to various tasks.