11 papers · 1 filter
No More Maybe-Arrows: Resolving Causal Uncertainty by Breaking Symmetries
Tingrui Huang, Devendra Singh Dhami
The recent works on causal discovery have followed a similar trend of learning partial ancestral graphs (PAGs) since observational data constrain the true causal directed acyclic g…
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Maurice Kraus, Felix Divo, Devendra Singh Dhami +1
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal a…
QuAnTS: Question Answering on Time Series
Felix Divo, Maurice Kraus, Anh Q. Nguyen +5
Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby allowing improved interactions with time series model…
Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
Harsh Poonia, Felix Divo, Kristian Kersting +1
Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between var…
Learning Differentiable Logic Programs for Abstract Visual Reasoning
Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami +1
Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been dev…
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…