From the 1 of 11 linked papers with an AI index.
11 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…
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…
BioDisco: Multi-agent hypothesis generation with dual-mode evidence, iterative feedback and temporal evaluation
Yujing Ke, Kevin George, Kathan Pandya +5
Identifying novel hypotheses is essential to scientific research, yet this process risks being overwhelmed by the sheer volume and complexity of available information. Existing aut…
MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Models
Asmita Sengupta, David Antony Selby, Sebastian Josef Vollmer +1
Knowledge graphs (KGs) are increasingly used to represent biomedical information in structured, interpretable formats. However, existing biomedical KGs often focus narrowly on mole…