AI is radically changing society and the main driver behind new AI methods and systems is machine learning. Machine learning focuses on finding solutions for, or patterns in, new data by learning from relevant existing data. Thus, machine learning algorithms are often applied to large datasets and then they more or less autonomously find good solutions by finding relevant information or patterns hidden in the data.
However, it is often not well understood why machine learning algorithms work so well in practice on completely new data – often their performance surpasses what current theory would suggest by a wide margin.
Being able to understand and predict when, why and how well machine learning algorithms work on a given problem is critical for knowing when they may be applied and trusted, in particular in more critical systems. Understanding why the algorithms work is also important to be able to drive the machine learning field forward in the right direction, improving upon existing algorithms and designing new ones.
Project period: 2020-2024
Budget: DKK 2,41 million
Project Manager
The goal of this project is to research and develop a better understanding of the generalization capability of the most used machine learning algorithms, including boosting algorithms, support vector machines, and deep learning algorithms. The result will be new generalization bounds, both showing positive what can be achieved and negative what cannot. This will allow us to more fully understand the current possibilities and limits, and thus drive the development of new and better methods. Ultimately, this will provide better guarantees for the quality of the output of machine learning algorithms in a variety of domains.
Value creation
Researching the theoretical foundation for machine learning (and thus essentially all AI-based systems) will benefit society at large since a solid theory will allow us to formally argue and understand when and under which conditions machine learning algorithms can deliver the required quality. As an added value, the project will bring together leading experts in Denmark in the theory of algorithms to (further) develop the fundamental theoretical basis of machine learning. Thus, it may serve as a starting point for additional national and international collaboration and projects, and it will build up competencies highly relevant for the Danish industry.
The project aims to enhance understanding of the generalization capabilities of key machine learning algorithms, leading to new generalization bounds that clarify their possibilities and limits, ultimately improving the quality and reliability of machine learning outputs across various domains.
