1 citations · 1 across the 9 of their papers we have counts for
9 papers
xWhyL: Causal Interactive Learning
Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig +4
Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite…
Tags for DAGs: Graph Refinement with Meta-Informed Relations
Florian Peter Busch, Moritz Willig, Florian Guldan +2
Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such external informatio…
Better Decisions through the Right Causal World Model
Elisabeth Dillies, Quentin Delfosse, Jannis Blüml +3
Reinforcement learning (RL) agents have shown remarkable performances in various environments, where they can discover effective policies directly from sensory inputs. However, the…
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng, Florian Peter Busch, Pooja Prasad +3
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the t…
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch +3
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping poi…
Where is the Truth? The Risk of Getting Confounded in a Continual World
Florian Peter Busch, Roshni Kamath, Rupert Mitchell +3
A dataset is confounded if it is most easily solved via a spurious correlation, which fails to generalize to new data. In this work, we show that, in a continual learning setting w…
Continual Causal Abstractions
Matej Zečević, Moritz Willig, Jonas Seng +1
This short paper discusses continually updated causal abstractions as a potential direction of future research. The key idea is to revise the existing level of causal abstraction t…
Attributions Beyond Neural Networks: The Linear Program Case
Florian Peter Busch, Matej Zečević, Kristian Kersting +1
Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exi…
Finding Structure and Causality in Linear Programs
Matej Zečević, Florian Peter Busch, Devendra Singh Dhami +1
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure…