92 citations · 108 across the 8 of their papers we have counts for
8 papers
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…
Towards Automated Causal Discovery: a case study on 5G telecommunication data
Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou +1
We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods.…
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
Giorgos Borboudakis, Paulos Charonyktakis, Konstantinos Paraschakis +1
AutoML platforms have numerous options for the algorithms to try for each step of the analysis, i.e., different possible algorithms for imputation, transformations, feature selecti…
A Meta-level Analysis of Online Anomaly Detectors
Antonios Ntroumpogiannis, Michail Giannoulis, Nikolaos Myrtakis +3
Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across indust…
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets
Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni +2
The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks. Most of the…
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Giorgos Borboudakis, Ioannis Tsamardinos
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus com…