4 papers
Feature Selection in Nonlinear SVMs via Local Search and Submodular Optimization
Federico D'Onofrio, Yuri Faenza, Laura Palagi
Embedded feature selection is a classical approach to interpretable machine learning, aiming to iden- tify the most informative variables while simultaneously training the predicti…
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi +6
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This sc…
Feature selection in linear SVMs via a hard cardinality constraint: a scalable SDP decomposition approach
Immanuel Bomze, Federico D'Onofrio, Laura Palagi +1
In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable…
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization
Corrado Coppola, Lorenzo Papa, Irene Amerini +1
Adaptive gradient methods have been increasingly adopted by deep learning community due to their fast convergence and reduced sensitivity to hyper-parameters. However, these method…