Computational & statistical learning
Our aim is to advance the frontiers of learning theory and machine learning, while building algorithmic tools for the analysis of complex systems and high dimensional data.
Research
From theory to practice: a multidisciplinary effort
MaLGa is primarily a center for fundamental research. A main feature of the research activity at MaLGa is the idea that a fruitful interplay between theoretical and applied questions is the key for long-lasting and impactful contributions. More broadly, this has always been the founding feature of ML studies at the University of Genoa. Our present and past research has always been equally split between foundational work in ML theory, and applied projects, working closely with partners such as hospitals or industries to develop vertical solutions.
Research topics
Computational Harmonic Analysis & Machine Learning
Our scientific interests focus on harmonic analysis, inverse problems, PDE and machine learning.
Machine Learning & Vision
We investigate different nuances of visual perception in artificial intelligence systems, where computer vision and machine learning are combined to obtain robust data-driven methods addressing a variety of problems.
Physics informed Machine Learning for biological Behavior
We blend physics with machine learning and biological behavior to ask how organisms strive in a fluid environment dominated by uncertainty.
Research projects
Most recent research projects
Sample complexity for inverse problems in PDE
Unit CHarMLLeading scientist Giovanni S. AlbertiRole within the project Principal InvestigatorDuration 2022 - 2027Sponsors EU | ERC StGTotal funding 1.15 M€- MARCH
Multi-modal learning for gait-based human analysis and authentication
Unit MLVLeading scientist Nicoletta Noceti, Francesca OdoneRole within the project Principal InvestigatorDuration 2023 - 2026Sponsors AFOSRTotal funding 299 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€Physics informed algorithms for sensing and navigating turbulent environments
Unit PiMLBLeading scientist Agnese SeminaraRole within the project Principal InvestigatorDuration 2021 - 2025Sponsors EU | ERC CoGTotal funding 1.98 M€
Publications
Most recent publications
| Title | Year | Author | Venue |
|---|---|---|---|
| Computer vision and deep learning meet plankton: Milestones and future directions | 2024 | Ciranni M.; Murino V.; Odone F.; Pastore V. P. | IMAGE AND VISION COMPUTING |
| RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement Learning | 2024 | Ceola Federico; Rosasco Lorenzo; Natale Lorenzo | IEEE ROBOTICS AND AUTOMATION LETTERS |
| Reconstitution of ORP-mediated lipid exchange coupled to PI4P metabolism | 2024 | Fuggetta Nicolas; Rigolli Nicola; Magdeleine Maud; Hamaï Amazigh; Seminara Agnese; Guillaume Drin And | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA |
| Assumption violations in causal discovery and the robustness of score matching | 2024 | Montagna Francesco; Mastakouri Atalanti A.; Eulig Elias; Noceti Nicoletta; Rosasco Lorenzo; Janzing Dominik; Aragam Bryon; Locatello Francesco | Advances in Neural Information Processing Systems |
| Head pose estimation with uncertainty and an application to dyadic interaction detection | 2024 | Figari Tomenotti Federico; Noceti Nicoletta; Odone Francesca | COMPUTER VISION AND IMAGE UNDERSTANDING |
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.