5 papers · 1 filter
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…
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…
CMA Light: a novel Minibatch Algorithm for large-scale non convex finite sum optimization
Corrado Coppola, Giampaolo Liuzzi, Laura Palagi
The supervised training of a deep neural network on a given dataset consists in the unconstrained minimization of the finite sum of continuously differentiable functions, commonly…
Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness
Ruggiero Seccia, Corrado Coppola, Giampaolo Liuzzi +1
In this work, we consider minimizing the average of a very large number of smooth and possibly non-convex functions, and we focus on two widely used minibatch frameworks to tackle…
Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta +2
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classific…