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Project type: SCITECH Project

Machine Learning Algorithms Generalisation

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 surpass 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 in order to be able drive the machine learning field forward in the right direction, improving upon existing algorithms and designing new ones.

The goal of this project is to research and develop a better understanding of the generalisation capability of the most used machine learning algorithms, including boosting algorithms, support vector machines and deep learning algorithms. The result will be new generalisation 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.

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 competences highly relevant for Danish industry.

October 1, 2020 – September 31, 2024 – 3,5 years.

Total budget DKK 2,41 / DIREC investment DKK 1,55

Participants

Project Manager

Kasper Green Larsen

Professor

Aarhus University
Department of Computer Science

E: larsen@cs.au.dk

Allan Grønlund

Postdoc

Aarhus University
Department of Computer Science

Mikkel Thorup

Professor

University of Copenhagen
Department of Computer Science

Martin Ritzert

Postdoc

Aarhus University
Department of Computer Science

Chenglin Fan

Postdoc

Aarhus University
Department of Computer Science

Partners