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Multi-Objective AutoML: Towards Accurate and Robust models

Title: Multi-Objective AutoML: Towards Accurate and Robust models Speaker: Jan van Rijn https://www.universiteitleiden.nl/en/... Abstract: Machine learning models have to adhere to many requirements. Beyond the need to be accurate and small enough to be deployed on satellite hardware, they need to be robust against various types of domain shifts. Automated machine learning has been successful in supporting data scientists in selecting appropriate machine learning architectures, as well as optimising hyperparameters. By doing so, data scientists can focus their attention on more important tasks. During the Horizon TAILOR project, we have seen a demand for AutoML techniques to not only provide solutions that are accurate but also those that are trustworthy according to several relevant criteria. Many models are known to be vulnerable to various types of input perturbations, whereas robustness against such deviations is an important criterion of trustworthiness. In this talk, I will summarise various projects we have done towards this goal, which envision AutoML solutions that specifically address the robustness of neural networks, but also include what hyperparameters are important to tune.