Lorenzo Rosasco

research

∙ 06/07/2023

Estimating Koopman operators with sketching to provably learn large scale dynamical systems

The theory of Koopman operators allows to deploy non-parametric machine ...

0 Giacomo Meanti, et al. ∙

research

∙ 06/06/2023

A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch

Multi-fingered robotic hands could enable robots to perform sophisticate...

0 Federico Ceola, et al. ∙

research

∙ 03/09/2023

Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test

We here propose a machine learning approach for monitoring particle dete...

0 Gaia Grosso, et al. ∙

research

∙ 02/10/2023

Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation

Fine-tuning and Domain Adaptation emerged as effective strategies for ef...

0 Andrea Maracani, et al. ∙

research

∙ 09/16/2022

Fine-tuning or top-tuning? Transfer learning with pretrained features and fast kernel methods

The impressive performances of deep learning architectures is associated...

0 Paolo Didier Alfano, et al. ∙

research

∙ 09/14/2022

Efficient Unsupervised Learning for Plankton Images

Monitoring plankton populations in situ is fundamental to preserve the a...

0 Paolo Didier Alfano, et al. ∙

research

∙ 08/02/2022

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Neural operators are a type of deep architecture that learns to solve (i...

0 Emilia Magnani, et al. ∙

research

∙ 06/27/2022

Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot

The visual system of a robot has different requirements depending on the...

0 Federico Ceola, et al. ∙

research

∙ 06/10/2022

Stochastic Zeroth order Descent with Structured Directions

We introduce and analyze Structured Stochastic Zeroth order Descent (S-S...

0 Marco Rando, et al. ∙

research

∙ 05/31/2022

AdaTask: Adaptive Multitask Online Learning

We introduce and analyze AdaTask, a multitask online learning algorithm ...

0 Pierre Laforgue, et al. ∙

research

∙ 05/27/2022

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

We study a class of dynamical systems modelled as Markov chains that adm...

0 Vladimir Kostic, et al. ∙

research

∙ 04/05/2022

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics...

0 Marco Letizia, et al. ∙

research

∙ 04/01/2022

Physics Informed Shallow Machine Learning for Wind Speed Prediction

The ability to predict wind is crucial for both energy production and we...

0 Daniele Lagomarsino Oneto, et al. ∙

research

∙ 03/16/2022

An elementary analysis of ridge regression with random design

In this note, we provide an elementary analysis of the prediction error ...

0 Jaouad Mourtada, et al. ∙

research

∙ 02/03/2022

Multiclass learning with margin: exponential rates with no bias-variance trade-off

We study the behavior of error bounds for multiclass classification unde...

0 Stefano Vigogna, et al. ∙

research

∙ 02/01/2022

Iterative regularization for low complexity regularizers

Iterative regularization exploits the implicit bias of an optimization a...

0 Cesare Molinari, et al. ∙

research

∙ 01/31/2022

Nyström Kernel Mean Embeddings

Kernel mean embeddings are a powerful tool to represent probability dist...

0 Antoine Chatalic, et al. ∙

research

∙ 01/30/2022

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Computing a Gaussian process (GP) posterior has a computational cost cub...

0 Daniele Calandriello, et al. ∙

research

∙ 01/17/2022

Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression

Kernel methods provide a principled approach to nonparametric learning. ...

0 Giacomo Meanti, et al. ∙

research

∙ 10/21/2021

Mean Nyström Embeddings for Adaptive Compressive Learning

Compressive learning is an approach to efficient large scale learning ba...

0 Antoine Chatalic, et al. ∙

research

∙ 09/20/2021

Understanding neural networks with reproducing kernel Banach spaces

Characterizing the function spaces corresponding to neural networks can ...

0 Francesca Bartolucci, et al. ∙

research

∙ 06/23/2021

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions

We introduce ParK, a new large-scale solver for kernel ridge regression....

0 Luigi Carratino, et al. ∙

research

∙ 06/16/2021

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization

Gaussian process optimization is a successful class of algorithms (e.g. ...

0 Marco Rando, et al. ∙

research

∙ 06/09/2021

From inexact optimization to learning via gradient concentration

Optimization was recently shown to control the inductive bias in a learn...

0 Bernhard Stankewitz, et al. ∙

research

∙ 04/29/2021

On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning

Balancing and push-recovery are essential capabilities enabling humanoid...

10 Diego Ferigo, et al. ∙

research

∙ 02/25/2021

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

With the recent advances in machine learning, problems that traditionall...

0 Gian Maria Marconi, et al. ∙

research

∙ 12/28/2020

Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics

Several object detection methods have recently been proposed in the lite...

7 Elisa Maiettini, et al. ∙

research

∙ 11/25/2020

Fast Object Segmentation Learning with Kernel-based Methods for Robotics

Object segmentation is a key component in the visual system of a robot t...

0 Federico Ceola, et al. ∙

research

∙ 11/25/2020

Fast Region Proposal Learning for Object Detection for Robotics

Object detection is a fundamental task for robots to operate in unstruct...

0 Federico Ceola, et al. ∙

research

∙ 07/01/2020

Decentralised Learning with Random Features and Distributed Gradient Descent

We investigate the generalisation performance of Distributed Gradient De...

11 Dominic Richards, et al. ∙

research

∙ 06/18/2020

Kernel methods through the roof: handling billions of points efficiently

Kernel methods provide an elegant and principled approach to nonparametr...

0 Giacomo Meanti, et al. ∙

research

∙ 06/17/2020

Regularized ERM on random subspaces

We study a natural extension of classical empirical risk minimization, w...

9 Andrea Della Vecchia, et al. ∙

research

∙ 06/17/2020

Interpolation and Learning with Scale Dependent Kernels

We study the learning properties of nonparametric ridge-less least squar...

0 Nicolò Pagliana, et al. ∙

research

∙ 06/17/2020

Implicit regularization for convex regularizers

We study implicit regularization for over-parameterized linear models, w...

0 Cesare Molinari, et al. ∙

research

∙ 06/11/2020

Asymptotics of Ridge(less) Regression under General Source Condition

We analyze the prediction performance of ridge and ridgeless regression ...

2 Dominic Richards, et al. ∙

research

∙ 05/28/2020

Hyperbolic Manifold Regression

Geometric representation learning has recently shown great promise in se...

34 Gian Maria Marconi, et al. ∙

research

∙ 02/23/2020

Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification

Gaussian processes (GP) are one of the most successful frameworks to mod...

2 Daniele Calandriello, et al. ∙

research

∙ 02/22/2020

Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms

We investigate numerically efficient approximations of eigenspaces assoc...

17 Cristian Rusu, et al. ∙

research

∙ 02/13/2020

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

We propose and analyze a novel theoretical and algorithmic framework for...

5 Carlo Ciliberto, et al. ∙

research

∙ 08/27/2019

Statistical and Computational Trade-Offs in Kernel K-Means

We investigate the efficiency of k-means in terms of both statistical an...

8 Daniele Calandriello, et al. ∙

research

∙ 07/18/2019

Fast approximation of orthogonal matrices and application to PCA

We study the problem of approximating orthogonal matrices so that their ...

11 Cristian Rusu, et al. ∙

research

∙ 07/11/2019

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling

In this paper, we propose and study a Nyström based approach to efficien...

10 Nicholas Sterge, et al. ∙

research

∙ 07/08/2019

Multi-Scale Vector Quantization with Reconstruction Trees

We propose and study a multi-scale approach to vector quantization. We d...

2 Enrico Cecini, et al. ∙

research

∙ 05/30/2019

Implicit Regularization of Accelerated Methods in Hilbert Spaces

We study learning properties of accelerated gradient descent methods for...

0 Nicolò Pagliana, et al. ∙

research

∙ 05/27/2019

Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces

We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian mani...

5 Ernesto De Vito, et al. ∙

research

∙ 03/13/2019

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret

Gaussian processes (GP) are a popular Bayesian approach for the optimiza...

4 Daniele Calandriello, et al. ∙

research

∙ 03/12/2019

Theory III: Dynamics and Generalization in Deep Networks

We review recent observations on the dynamical systems induced by gradie...

0 Andrzej Banburski, et al. ∙

research

∙ 02/22/2019

Beating SGD Saturation with Tail-Averaging and Minibatching

While stochastic gradient descent (SGD) is one of the major workhorses i...

0 Nicole Mücke, et al. ∙

research

∙ 02/18/2019

A computational model for grid maps in neural populations

Grid cells in the entorhinal cortex, together with place, speed and bord...

0 Fabio Anselmi, et al. ∙

research

∙ 10/31/2018

On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate...

24 Alessandro Rudi, et al. ∙

Lorenzo Rosasco

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