6 papers
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing contr…
Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure lea…
Stereovision Image Processing for Planetary Navigation Maps with Semi-Global Matching and Superpixel Segmentation
Yan-Shan Lu, Miguel Arana-Catania, Saurabh Upadhyay +1
Mars exploration requires precise and reliable terrain models to ensure safe rover navigation across its unpredictable and often hazardous landscapes. Stereoscopic vision serves a…
Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges
Miguel Arana-Catania, Weisi Guo
Causal understanding is important in many disciplines of science and engineering, where we seek to understand how different factors in the system causally affect an experiment or s…
A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
Konstantinos Platanitis, Miguel Arana-Catania, Saurabh Upadhyay +1
This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of th…
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
Ä°rem Ãstek, Miguel Arana-Catania, Alexander Farr +1
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wil…