413 citations · 2k across the 87 of their papers we have counts for
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Assessing high-order effects in feature importance via predictability decomposition
Marlis Ontivero-Ortega, Luca Faes, Jesus M Cortes +2
Leveraging the large body of work devoted in recent years to describe redundancy and synergy in multivariate interactions among random variables, we propose a novel approach to qua…
Disentangling high order effects in the transfer entropy
Sebastiano Stramaglia, Luca Faes, Jesus M. Cortes +1
Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwis…
Quantifying high-order interdependencies on individual patterns via the local O-information: theory and applications to music analysis
Tomas Scagliarini, Daniele Marinazzo, Yike Guo +2
High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer,…
Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality
Alessandro Montalto, Sebastiano Stramaglia, Luca Faes +3
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dy…
Information flow in a network model and the law of diminishing marginal returns
Daniele Marinazzo, Mario Pellicoro, Guorong Wu +2
We analyze a simple dynamical network model which describes the limited capacity of nodes to process the input information. For a suitable choice of the parameters, the information…
Causal information approach to partial conditioning in multivariate data sets
Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia
When evaluating causal influence from one time series to another in a multivariate dataset it is necessary to take into account the conditioning effect of the other variables. In t…
Extending Granger causality to nonlinear systems
Nicola Ancona, Daniele Marinazzo, Sebastiano Stramaglia
We consider extension of Granger causality to nonlinear bivariate time series. In this frame, if the prediction error of the first time series is reduced by including measurements…