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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 ...
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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...
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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...
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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...
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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...
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09/14/2022
Efficient Unsupervised Learning for Plankton Images
Monitoring plankton populations in situ is fundamental to preserve the a...
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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...
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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...
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06/10/2022
Stochastic Zeroth order Descent with Structured Directions
We introduce and analyze Structured Stochastic Zeroth order Descent (S-S...
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05/31/2022
AdaTask: Adaptive Multitask Online Learning
We introduce and analyze AdaTask, a multitask online learning algorithm ...
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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...
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04/05/2022
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics...
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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...
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03/16/2022
An elementary analysis of ridge regression with random design
In this note, we provide an elementary analysis of the prediction error ...
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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...
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02/01/2022
Iterative regularization for low complexity regularizers
Iterative regularization exploits the implicit bias of an optimization a...
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01/31/2022
Nyström Kernel Mean Embeddings
Kernel mean embeddings are a powerful tool to represent probability dist...
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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...
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01/17/2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Kernel methods provide a principled approach to nonparametric learning. ...
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10/21/2021
Mean Nyström Embeddings for Adaptive Compressive Learning
Compressive learning is an approach to efficient large scale learning ba...
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09/20/2021
Understanding neural networks with reproducing kernel Banach spaces
Characterizing the function spaces corresponding to neural networks can ...
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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....
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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. ...
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06/09/2021
From inexact optimization to learning via gradient concentration
Optimization was recently shown to control the inductive bias in a learn...
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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...
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02/25/2021
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
With the recent advances in machine learning, problems that traditionall...
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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...
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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...
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11/25/2020
Fast Region Proposal Learning for Object Detection for Robotics
Object detection is a fundamental task for robots to operate in unstruct...
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07/01/2020
Decentralised Learning with Random Features and Distributed Gradient Descent
We investigate the generalisation performance of Distributed Gradient De...
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06/18/2020
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametr...
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06/17/2020
Regularized ERM on random subspaces
We study a natural extension of classical empirical risk minimization, w...
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06/17/2020
Interpolation and Learning with Scale Dependent Kernels
We study the learning properties of nonparametric ridge-less least squar...
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06/17/2020
Implicit regularization for convex regularizers
We study implicit regularization for over-parameterized linear models, w...
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06/11/2020
Asymptotics of Ridge(less) Regression under General Source Condition
We analyze the prediction performance of ridge and ridgeless regression ...
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05/28/2020
Hyperbolic Manifold Regression
Geometric representation learning has recently shown great promise in se...
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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...
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02/22/2020
Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms
We investigate numerically efficient approximations of eigenspaces assoc...
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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...
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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...
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07/18/2019
Fast approximation of orthogonal matrices and application to PCA
We study the problem of approximating orthogonal matrices so that their ...
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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...
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07/08/2019
Multi-Scale Vector Quantization with Reconstruction Trees
We propose and study a multi-scale approach to vector quantization. We d...
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05/30/2019
Implicit Regularization of Accelerated Methods in Hilbert Spaces
We study learning properties of accelerated gradient descent methods for...
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05/27/2019
Reproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces
We study reproducing kernel Hilbert spaces (RKHS) on a Riemannian mani...
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03/13/2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Gaussian processes (GP) are a popular Bayesian approach for the optimiza...
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03/12/2019
Theory III: Dynamics and Generalization in Deep Networks
We review recent observations on the dynamical systems induced by gradie...
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02/22/2019
Beating SGD Saturation with Tail-Averaging and Minibatching
While stochastic gradient descent (SGD) is one of the major workhorses i...
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02/18/2019
A computational model for grid maps in neural populations
Grid cells in the entorhinal cortex, together with place, speed and bord...
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10/31/2018