Saverio Salzo received a MSc degree in (pure) Mathematics from the University of Bari in 2001 (summa cum laude) and a PhD in Computer Science from the University of Genova in 2012. His main research interests are in nonsmooth optimization, proximal splitting methods, stochastic algorithms, hyperparameter optimization, bilevel optimization, optimization in probability spaces, support vector machines in Banach spaces, and tensor kernel methods. From 2016 to 2018 he was member of the Laboratory for Computational and Statistical Learning (LCSL), which is a joint initiative between the Italian Institute of Technology and the Massachusetts Institute of Technology. He has been visiting scholar at KU Leuven, Belgium and since 2020 he is honorary lecturer at University College London (UCL). In 2021 he achieved habilitation as associate professor in operational research and numerical analysis from the Italian Ministry for Education, University and Research. Since July 2020 he is assistant professor (tenured) at DIAG, Department of Computer, Control and Management Engineering of Sapienza University Rome.
Phone
+39 010 2897 413
Research center
CHT@Erzelli
About
All Publications
2025
Mezidi A-R., Patracone J., Salzo S., Emonet R., Sebban M.
A Bregman Proximal Viewpoint on Neural Operators
13th International Conference on Learning Representations, ICLR 2025, vol. 267
Conference Paper
Conference
2025
Grazzi R., Pontil M., Salzo S.
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
Proceedings of the 42nd International Conference on Machine Learning, vol. 267
Article
Journal
2025
Venturini S., De Santis M., Patracone J., Schmidt M., Rinaldi F., Salzo S.
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization
Transactions on Machine Learning Research, pp. 27
Article
Journal
2023
Kostic V.R., Salzo S.
The method of randomized Bregman projections for stochastic feasibility problems
Numerical Algorithms, vol. 93, (no. 3), pp. 1269-1307
2022
Salzo S., Villa S.
Parallel random block-coordinate forward–backward algorithm: a unified convergence analysis
Mathematical Programming, Series B, vol. 193, (no. 1), pp. 225-269
Colleagues of Computational Statistics and Machine Learning