5 papers
Tagged for Direction: Pinning Down Causal Edge Directions with Precision
Florian Peter Busch, Moritz Willig, Florian Guldan +2
Not every causal relation between variables is equal, and this can be leveraged for the task of causal discovery. Recent research shows that pairs of variables with particular type…
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…
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…
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…