3 papers
cs.LG2026
Adversarial Causal Tuning for Realistic Time-series Generation
Nikolaos Gkorgkolis, Nikolaos Kougioulis, MingXue Wang +4
We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional distributions as a given real dat…
cs.LG2026
Large Causal Models for Temporal Causal Discovery
Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang +4
Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an…
cs.LG2024
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis +1
Any supervised machine learning analysis is required to provide an estimate of the out-of-sample predictive performance. However, it is imperative to also provide a quantification…