Lorenzo Rosasco
- Generalization Properties of Learning with Random Features
2016/02/14 by Alessandro Rudi, Rudi, Alessandro, Lorenzo Rosasco +1 · 14 citations
Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms
- Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
2022/05/27 by Vladimir R. Kostic, Pietro Novelli, Kostic, Vladimir +9 · 14 citations
Computer Science · Engineering · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Model Reduction and Neural Networks
- Kernel methods through the roof: handling billions of points efficiently
2020/06/18 by Giacomo Meanti, Meanti, Giacomo, Luigi Carratino +5 · 9 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- Less is More: Nyström Computational Regularization
2015/07/16 by Alessandro Rudi, Raffaello Camoriano, Rudi, Alessandro +3 · 8 citations
Engineering · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques
- Learning Probability Measures with respect to Optimal Transport Metrics
2012/09/05 by Guillermo D. Cañas, Canas, Guillermo D., Lorenzo Rosasco +1 · 4 citations
Computer Science · Mathematics · #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Bayesian Modeling and Causal Inference
- Theory of Deep Learning III: explaining the non-overfitting puzzle
2017/12/30 by Tomaso Poggio, Poggio, Tomaso, Kenji Kawaguchi +13 · 9 citations
Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
- Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
2022/08/02 by Emilia Magnani, Magnani, Emilia, Nicholas Krämer +7 · 5 citations
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Reservoir Engineering and Simulation Methods
- Multiclass Learning with Simplex Coding
2012/09/06 by Youssef Mroueh, Mroueh, Youssef, Tomaso Poggio +5 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques
- Understanding neural networks with reproducing kernel Banach spaces
2021/09/20 by Francesca Bartolucci, Bartolucci, Francesca, Ernesto De Vito +5 · 3 citations
Computer Science · Engineering · #Advanced Numerical Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #Functional Analysis (math.FA) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Medical Imaging and Analysis
- Stochastic Zeroth order Descent with Structured Directions
2022/06/10 by Marco Rando, Rando, Marco, Cesare Molinari +5 · 3 citations
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
- Unsupervised Learning of Invariant Representations in Hierarchical\n Architectures
2013/11/17 by Fabio Anselmi, Joel Z. Leibo, Anselmi, Fabio +9 · 4 citations
Computer Science · Engineering · Neuroscience · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face Recognition and Perception #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Remote-Sensing Image Classification #Visual perception and processing mechanisms
- Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
2023/03/09 by G. Grosso, Grosso, Gaia, Nicolò Lai +13 · 4 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies
- An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
2023/05/25 by Marco Rando, Rando, Marco, Cesare Molinari +5 · 3 citations
Computer Science · Engineering · #90C25 #90C26 #90C30 (Secondary) #90C56 (Primary) 49J52 #FOS: Mathematics #G.1.6 #Machine Learning and ELM #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- Estimating Koopman operators with sketching to provably learn large scale dynamical systems
2023/06/07 by Giacomo Meanti, Antoine Chatalic, Meanti, Giacomo +9 · 3 citations
Computer Science · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Model Reduction and Neural Networks #Neural Networks and Applications
- Iterate averaging as regularization for stochastic gradient descent
2018/02/22 by Gergely Neu, Lorenzo Rosasco, Neu, Gergely +1 · 2 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques
- On Fast Leverage Score Sampling and Optimal Learning
2018/10/31 by Alessandro Rudi, Daniele Calandriello, Rudi, Alessandro +5 · 1 citation
Computer Science · Engineering · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- Implicit Regularization of Accelerated Methods in Hilbert Spaces
2019/05/30 by Nicolò Pagliana, Lorenzo Rosasco, Pagliana, Nicolò +1 · 1 citation
Engineering · Mathematics · Computer Science · #Sparse and Compressive Sensing Techniques #Numerical methods in inverse problems #Stochastic Gradient Optimization Techniques
- Learning to predict target location with turbulent odor plumes
2021/06/16 by Nicola Rigolli, Nicodemo Magnoli, Rigolli, Nicola +5 · 1 citation
Agricultural and Biological Sciences · Engineering · Neuroscience · #Advanced Chemical Sensor Technologies #FOS: Biological sciences #Insect Pheromone Research and Control #Olfactory and Sensory Function Studies #Quantitative Methods (q-bio.QM)
- Mean Nyström Embeddings for Adaptive Compressive Learning
2021/10/21 by Antoine Chatalic, Chatalic, Antoine, Luigi Carratino +5 · 1 citation
Computer Science · Engineering · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques
- Learning Multiple Visual Tasks while Discovering their Structure
2015/04/13 by Carlo Ciliberto, Ciliberto, Carlo, Lorenzo Rosasco +3 · 2 citations
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sparse and Compressive Sensing Techniques
- A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
2025/02/19 by Antoine Chatalic, Marco Letizia, Chatalic, Antoine +5 · 2 citations
Computer Science · Engineering · Mathematics · #Face and Expression Recognition #Sparse and Compressive Sensing Techniques #Advanced Statistical Methods and Models
- Neural reproducing kernel Banach spaces and representer theorems for deep networks
2024/03/13 by Francesca Bartolucci, Bartolucci, Francesca, Ernesto De Vito +5 · 1 citation
Computer Science · #FOS: Computer and information sciences #FOS: Mathematics #Functional Analysis (math.FA) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Fast and Fourier Features for Transfer Learning of Interatomic Potentials
2025/05/08 by Pietro Novelli, Giacomo Meanti, Novelli, Pietro +10 · 3 citations
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Quantum many-body systems
- PIKS: Universal Physics-Informed Kernel Methods
2026/07/29 by Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Computer Science · Mathematics · #cs.LG #stat.ML
- Learning Ergodic Dynamical Systems from a Finite Trajectory
2026/07/24 by Oleksii Kachaiev, Silvia Villa, Lorenzo Rosasco
#stat.ML #cs.LG
- Langevin for Nonconvex Optimization: Exact, Inexact and Zeroth-Order
2026/07/24 by Emanuele Naldi, Marco Rando, Lorenzo Rosasco +1
#math.OC