Where is the Truth? The Risk of Getting Confounded in a Continual World

1 code implementation • 9 Feb 2024 • Florian Peter Busch, Roshni Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt

A dataset is confounded if it is most easily solved via a spurious correlation which fails to generalize to new data.

Continual Learning valid

Continual Causal Abstractions

no code implementations • 23 Dec 2022 • Matej Zečević, Moritz Willig, Jonas Seng, Florian Peter Busch

This short paper discusses continually updated causal abstractions as a potential direction of future research.

Attributions Beyond Neural Networks: The Linear Program Case

no code implementations • 14 Jun 2022 • Florian Peter Busch, Matej Zečević, Kristian Kersting, Devendra Singh Dhami

We introduce an approach where we consider neural encodings for LPs that justify the application of attribution methods from explainable artificial intelligence (XAI) designed for neural learning systems.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Finding Structure and Causality in Linear Programs

1 code implementation • 29 Mar 2022 • Matej Zečević, Florian Peter Busch, Devendra Singh Dhami, Kristian Kersting

Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems.

BIG-bench Machine Learning