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Mixing Deep Learning and Multiple Criteria Optimization: An Application to Distributed Learning with Multiple Datasets
In the case of labeled data and supervised learning, machine training consists in minimizing the loss function subject to different constraints.
Multicriteria interpretability driven Deep Learning
We propose a Multicriteria technique that allows to control the feature effects on the model's outcome by injecting knowledge in the objective function.
Federated Deep Learning in Electricity Forecasting: An MCDM Approach
In fact, Deep Learning models require plenty of resources, and distributed training is needed.
Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default
Academic research and the financial industry have recently paid great attention to Machine Learning algorithms due to their power to solve complex learning tasks.
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