Resource Efficient Machine Learning
This project aims to develop efficient machine learning solutions by blending statistical and algorithmic principles to design scalable learning machines.
LCSL
The Laboratory for Computational and Statistical Learning (LCSL) is one of the research units within MaLGa, the machine learning center of the University of Genova. LCSL focuses on the development of efficient and reliable machine learning algorithms blending tools from statistics, optimization, and regularization theory.
Our goal is to develop theoretically grounded and practical machine learning algorithms. In collaboration with other MaLGa units and external collaborators we work on foundational aspects of Machine Learning (CHARML) and also a number of applications including vision (MLV), robotics, and biological behavior (PiMLB).
Research topics
Optimization for Machine Learning
We aim to develop flexible and scalable optimization algorithms for machine learning, emphasizing robustness and adaptivity to tackle diverse and complex data-driven problems effectively.
Structured Machine Learning
We tackle the challenge of handling structured data in machine learning, addressing various domains and tasks such as text structure inference, subgraph identification, and time series forecasting.
Explainable and Reproducible Machine Learning
Machine Learning for Health
People
Annalisa
BarlaSimone
Di MarinoCesare
MolinariLorenzo
RosascoSilvia
VillaEdoardo
CaldarelliArnaud
WatusadisiIlaria
StanzaniMarco
LetiziaHippolyte
LabarrièreShuo
HuangPietro
ZerbettoJoachim
Bona-Pellis…Lorenzo
FioritoOleksii
KachaievGiordano
VitaleLuc
Brogat-MotteLuca
Piccaluga
Past People
People
Marco Rando | 2023 → 2025 | Post-doctoral fellow
Emanuele Naldi | 2023 → 2025 | Post-doctoral fellow | Optimization in infinite dimensional spaces
Francesco Montagna | 2021 → 2025 | PhD student
Emilia Magnani | 2024 | PhD student
Cheik Traoré | 2023 → 2024 | Post-doctoral fellow | Optimization
Stefano Ravera | 2022 → 2024 | Research Scholar | Data handling for large-scale AI applications
Rayan Autones | 2024 | Student
Andrea Della Vecchia | 2022 → 2024 | Post-doctoral fellow | Machine Learning
Elena Milano | 2024 | Research Scholar | Content strategy for ethical-AI communication
Ettore Fincato | 2023 → 2024 | PhD student
Cristian Jesus Vega Cereno | 2021 → 2024 | PhD student | Optimization
Gabriele Bortolai | 2024 | Student | Machine Learning and Vision
Jonathan Chirinos Rodriguez | 2024 | PhD student
Antoine Chatalic | 2021 → 2024 | Post-doctoral fellow
Rosanna Turrisi | 2023 → 2024 | Post-doctoral fellow | Machine Learning
Elisa Maiettini | 2021 → 2023 | Post-doctoral fellow
Nicolas Schreuder | 2020 → 2023 | Post-doctoral fellow
Vassilis Apidopoulos | 2019 → 2023 | Post-doctoral fellow
Federico Ceola | 2020 → 2023 | PhD student
Andrea Maracani | 2020 → 2023 | PhD student
Research projects
Most recent LCSL research projects
- ELIAS
European Lighthouse of AI for Sustainability
Unit LCSLLeading scientist Lorenzo RosascoRole within the project CollaboratorDuration 2023 - 2027Sponsors HORIZON-RIATotal funding 105 k€ - SLING
Efficient algorithms for sustainalble machine learning
Unit LCSLLeading scientist Lorenzo RosascoRole within the project Principal InvestigatorDuration 2019 - 2025Sponsors EU | ERC CoGTotal funding 1.98 M€ - ML4IP
Reliable and efficient machine learning for inverse problems
Unit LCSLLeading scientist Lorenzo RosascoRole within the project Principal InvestigatorDuration 2022 - 2025Sponsors FARE 2020Total funding 300 k€ Integrating network science and explainable AI to forecast the impact of climate change on infectious disease risk
Theme Machine Learning for HealthUnit LCSLLeading scientist Annalisa BarlaRole within the project Head of unitDuration 2023 - 2025Sponsors MIUR ¦ PRINTotal funding 250 k€
Publications
Most recent LCSL publications
| Title | Year | Author | Venue |
|---|---|---|---|
| Artificial intelligence and network science as tools to illustrate academic research evolution in interdisciplinary fields: The case of Italian design | 2025 | Pretolesi Daniele; Stanzani Ilaria; Ravera Stefano; Vian Andrea; Barla Annalisa | PLOS ONE |
| Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method | 2025 | Caldarelli E.; Chatalic A.; Colome A.; Molinari C.; Ocampo-Martinez C.; Torras C.; Rosasco L. | AUTOMATICA |
| Grand-Canonical Optimal Transport | 2025 | DI MARINO Simone; Lewin Mathieu; Nenna Luca | ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS |
| Representation theorems for normed modules | 2025 | Di Marino S.; Lucic D.; Pasqualetto E. | REVISTA DE LA REAL ACADEMIA DE CIENCIAS EXACTAS, FÍSICAS Y NATURALES. SERIE A, MATEMÁTICAS |
| Be greedy and learn: Efficient and certified algorithms for parametrized optimal control problems | 2025 | Molinari Cesare; Kleikamp H.; Lazar Martin | ESAIM. MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS |
Open data & software
- LCSL
CRiSP for Misspecified Robot Model
Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, and Carlo CilibertoCode accompanying the paper “Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models”
- LCSL
Iterreg: Iterative Regularization For Linear Models
C. Molinari, M. Massias, L. Rosasco and S. VillaIterreg is a scikit-learn compatible python package to perform iterative regularization of linear models. It implements the algorithm of “Iterative regularization for convex regularizers” by C. Molinari, M. Massias, L. Rosasco and S. Villa, AISTATS 2021.
- LCSL
Falkon 2.0: Scaling Nyström KRR to Billions of Points
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro RudiPython package implementing the Falkon algorithm for large-scale, approximate kernel ridge regression. The implementation is based on PyTorch and runs on CPU and GPU.
- LCSL
Batch-BKB: Near-linear time, no regret Bayesian optimization
Calandriello Daniele, Luigi Carratino, Alessandro Lazaric, Michal Valko and Lorenzo RosascoPython code implementing Batch-BKB: the first Bayesian optimization (a.k.a. Gaussian process or bandit optimization) algorithm that is both provably no-regret and guaranteed to run in near-linear time time.