Foundations for learning transferable concepts
Research Project , 2020 – 2024

Transfer learning (TL) studies the automatic acquisition and application of transferable knowl- edge (Pan and Yang, 2009). In recent years, a large body of (primarily empirical) research has studied TL in the context of prediction, using algorithms built on surprisingly shaky mathemat- ical foundations (as discussed in Johansson et al. (2019); Zhao et al. (2019)). While there are known conditions that guarantee successful TL, these are often violated in fundamental ways in practice. This project aims to address this gap by providing rigorous, plausible and sufficient conditions for TL and practical learning algorithms that make use of them as assumptions. The hired student will pursue a PhD in machine learning within computer science and engineering.

Participants

Fredrik Johansson (contact)

Assistant Professor at Chalmers, Computer Science and Engineering (Chalmers), Data Science

Funding

Wallenberg AI, Autonomous Systems and Software Program

Funding Chalmers participation during 2020–2024

Related Areas of Advance and Infrastructure

Information and Communication Technology

Areas of Advance

More information

Latest update

2020-08-06