Several recent works encourage the use of a Bayesian framework when asse...
Human lives are increasingly being affected by the outcomes of automated...
The statistical characteristics of instance-label pairs often change wit...
In this paper we evaluate how constructive heuristics degrade when a pro...
Reliable deployment of machine learning models such as neural networks
c...
The reasons why Deep Neural Networks are susceptible to being fooled by
...
Load forecasting is crucial for multiple energy management tasks such as...
The orienteering problem is a route optimization problem which consists ...
In this paper, we extend techniques developed in the context of the
Trav...
Despite the remarkable performance and generalization levels of deep lea...
Recent advances in technology have brought major breakthroughs in data
c...
We propose and develop a novel framework for analyzing permutation-based...
Many Pareto-based multi-objective evolutionary algorithms require to ran...
Time series classification is an increasing research topic due to the va...
The analysis of continously larger datasets is a task of major importanc...
Although a great methodological effort has been invested in proposing
co...
NM-landscapes have been recently introduced as a class of tunable rugged...
The minimum common string partition problem is an NP-hard combinatorial
...
This paper shows how the Bayesian network paradigm can be used in order ...