From the 1 of 13 linked papers with an AI index.
13 papers
CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
Gerrit Großmann, Sumantrak Mukherjee, Sebastian J. Vollmer
Learning fine-grained spatial patterns from coarse-resolution data is challenging, especially in causal settings where high-resolution effects must be inferred from aggregated inte…
Orca: Neural Operators for Causal Reasoning in Continuous Time
Gerrit GroÃmann, David A. Selby, Sebastian J. Vollmer
The paper introduces Orca, a neural‑operator based framework that models each node in a causal graph as a time‑varying function, enabling causal and counterfactual reasoning for sy…
When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting
Yahya Aalaila, Mouad Elhamdi, Gerrit GroÃmann +3
Spatio-temporal point-process models must often generalise across space when local event histories are sparse. We study whether exogenous spatial context can compensate in such reg…
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
Yahya Aalaila, Gerrit GroÃmann, Sebastian Vollmer
Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Recent neural STPPs span expre…
HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity
Yahya Aalaila, Sumantrak Mukherjee, Gerrit GroÃmann +1
Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult…
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning
Yousef Koka, David Selby, Gerrit GroÃmann +2
Data preprocessing is often paid little attention in machine learning, despite its potentially significant impact on model performance. While automated machine learning pipelines a…