92 citations · 156 across the 15 of their papers we have counts for
16 papers
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…
On Predictive Explanation of Data Anomalies
Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides
Numerous algorithms have been proposed for detecting anomalies (outliers, novelties) in an unsupervised manner. Unfortunately, it is not trivial, in general, to understand why a gi…
Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data
Anastasios Tsourtis, Yannis Pantazis, Ioannis Tsamardinos
Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial ma…
A generalised OMP algorithm for feature selection with application to gene expression data
Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki +1
Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest. To apply to molec…
Multiple Equivalent Solutions for the Lasso
Yannis Pantazis, Vincenzo Lagani, Paulos Charonyktakis +1
Feature selection is an important problem studied in data analytics seeking to identify a minimal-size feature subset that is optimally predictive for an outcome of interest. It is…
A Unified Approach for Sparse Dynamical System Inference from Temporal Measurements
Yannis Pantazis, Ioannis Tsamardinos
Temporal variations in biological systems and more generally in natural sciences are typically modelled as a set of Ordinary, Partial, or Stochastic Differential or Difference Equa…
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation
Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris +1
Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of…
Massively-Parallel Feature Selection for Big Data
Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis +2
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the chal…
Forward-Backward Selection with Early Dropping
Giorgos Borboudakis, Ioannis Tsamardinos
Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types…
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…
Discovering and Exploiting Entailment Relationships in Multi-Label Learning
Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos
This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships…
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
Sofia Triantafillou, Ioannis Tsamardinos
Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under d…
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…
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs
Giorgos Borboudakis, Ioannis Tsamardinos
We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to dir…