6 papers
"So, Tell Me About Your Policy...": Distillation of interpretable policies from Deep Reinforcement Learning agents
Giovanni Dispoto, Paolo Bonetti, Marcello Restelli
Recent advances in Reinforcement Learning (RL) largely benefit from the inclusion of Deep Neural Networks, boosting the number of novel approaches proposed in the field of Deep Rei…
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
Davide Salaorni, Vincenzo De Paola, Samuele Delpero +9
In recent years, \emph{Reinforcement Learning} (RL) has made remarkable progress, achieving superhuman performance in a wide range of simulated environments. As research moves towa…
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. Previous works have…
Causal Feature Selection via Transfer Entropy
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance…
Nonlinear Feature Aggregation: Two Algorithms driven by Theory
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
Many real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues, as well as the risk of overfitting. Ideall…
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis
Paolo Bonetti, Alberto Maria Metelli, Marcello Restelli
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should select only the relevant, non-redu…