A Gray-Box Approach for Curriculum Learning. - Francesco Foglino; Matteo Leonetti; Simone Sagratella; Ruggiero Seccia (WCGO, 2019)

Block layer decomposition schemes for training deep neural networks. - Laura Palagi; Ruggiero Seccia (J. Glob. Optim., 2020)

On the convergence of a Block-Coordinate Incremental Gradient method. - Laura Palagi; Ruggiero Seccia (Soft Comput., 2021)

Convergence under Lipschitz smoothness of ease-controlled Random Reshuffling gradient Algorithms. - Giampaolo Liuzzi; Laura Palagi; Ruggiero Seccia (CoRR, 2022)

Machine Learning Applications in Route Planning for Attended Home Delivery. - Thomas R. Visser; Ruggiero Seccia; Wouter Merkx; Wouter Kool 001; Leendert Kok (BNAIC/BENELEARN, 2023)

Convergence of ease-controlled random reshuffling gradient algorithms under Lipschitz smoothness. - Ruggiero Seccia; Corrado Coppola; Giampaolo Liuzzi; Laura Palagi (Comput. Optim. Appl., 2025)