18 citations · 70 across the 18 of their papers we have counts for
12 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…
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…
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…
Benders decomposition for congested partial set covering location with uncertain demand
Alice Calamita, Ivana Ljubić, Laura Palagi
In this paper, we introduce a mixed integer quadratic formulation for the congested variant of the partial set covering location problem, which involves determining a subset of fac…
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…
A computational study of off-the-shelf MINLP solvers on a benchmark set of congested capacitated facility location problems
Pasquale Avella, Alice Calamita, Laura Palagi
This paper analyzes the performance of five well-known off-the-shelf optimization solvers on a set of congested capacitated facility location problems formulated as mixed-integer c…
Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree
Giulia Di Teodoro, Marta Monaci, Laura Palagi
The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. Tree ensemble methods, su…
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…
Margin Optimal Classification Trees
Federico D'Onofrio, Giorgio Grani, Marta Monaci +1
In recent years, there has been growing attention to interpretable machine learning models which can give explanatory insights on their behaviour. Thanks to their interpretability,…
Solving the vehicle routing problem with deep reinforcement learning
Simone Foa, Corrado Coppola, Giorgio Grani +1
Recently, the applications of the methodologies of Reinforcement Learning (RL) to NP-Hard Combinatorial optimization problems have become a popular topic. This is essentially due t…
Block Layer Decomposition schemes for training Deep Neural Networks
Laura Palagi, Ruggiero Seccia
Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers,…
A Class of Parallel Decomposition Algorithms for SVMs Training
Andrea Manno, Laura Palagi, Simone Sagratella
The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms g…