4 accepted papers
Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity …
Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning techniques have been proposed in the literature, there is one…
Synthetic time series are often used in practical applications to augment the historical time series dataset, amplify the occurrence of rare events and also create counterfactual scenarios. Distributional-similarity (which we refer to as realism) as well as the satisfaction of certain numerical con…
Many safety critical scenarios, including post-disaster areas, or military fields, require prompt area monitoring and fast detection of events of interest. Flying Ad-hoc Networks (FANETs) provide a powerful tool to search the area, and locate anomalies. Nevertheless, wide-area deployment of FANETs p…