Search Results for author: Carlo Ciliberto

Found 43 papers, 12 papers with code

Measuring dissimilarity with diffeomorphism invariance

1 code implementation11 Feb 2022 Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi

Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms.

Distribution Regression with Sliced Wasserstein Kernels

no code implementations8 Feb 2022 Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto

We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds.

The Role of Global Labels in Few-Shot Classification and How to Infer Them

no code implementations NeurIPS 2021 Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto

Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.

Few-Shot Learning

Conditional Meta-Learning of Linear Representations

no code implementations30 Mar 2021 Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto

Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks.

Meta-Learning Representation Learning

Adversarial Imitation Learning with Trajectorial Augmentation and Correction

no code implementations25 Mar 2021 Dafni Antotsiou, Carlo Ciliberto, Tae-Kyun Kim

Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks.

Data Augmentation Imitation Learning

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

1 code implementation25 Feb 2021 Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto

Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map.

Structured Prediction

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning

1 code implementation NeurIPS 2020 Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto

However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks’ distribution cannot be captured by a single meta- parameter vector.

Meta-Learning

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning

no code implementations25 Aug 2020 Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto

However, these methods may perform poorly on heterogeneous environments of tasks, where the complexity of the tasks' distribution cannot be captured by a single meta-parameter vector.

Meta-Learning

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning

no code implementations29 Jul 2020 Giulia Luise, Massimiliano Pontil, Carlo Ciliberto

The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation.

Hyperbolic Manifold Regression

no code implementations28 May 2020 Gian Maria Marconi, Lorenzo Rosasco, Carlo Ciliberto

Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models.

Relational Reasoning Representation Learning

Structured Prediction for Conditional Meta-Learning

1 code implementation NeurIPS 2020 Ruohan Wang, Yiannis Demiris, Carlo Ciliberto

We derive task-adaptive structured meta-learning (TASML), a principled framework that yields task-specific objective functions by weighing meta-training data on target tasks.

Few-Shot Learning Structured Prediction

Support-weighted Adversarial Imitation Learning

no code implementations20 Feb 2020 Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris

To address the challenges, we propose Support-weighted Adversarial Imitation Learning (SAIL), a general framework that extends a given AIL algorithm with information derived from support estimation of the expert policies.

Imitation Learning

Statistical Limits of Supervised Quantum Learning

no code implementations28 Jan 2020 Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi, Leonard Wossnig

Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy.

Learning Theory

Online-Within-Online Meta-Learning

1 code implementation NeurIPS 2019 Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil

We study the problem of learning a series of tasks in a fully online Meta-Learning setting.

Meta-Learning

Support-guided Adversarial Imitation Learning

no code implementations25 Sep 2019 Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris

We propose Support-guided Adversarial Imitation Learning (SAIL), a generic imitation learning framework that unifies support estimation of the expert policy with the family of Adversarial Imitation Learning (AIL) algorithms.

Imitation Learning

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

1 code implementation NeurIPS 2019 Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto

We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence.

Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation

2 code implementations16 May 2019 Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori, Yiannis Demiris

We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.

Imitation Learning reinforcement-learning

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization

1 code implementation25 Mar 2019 Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil

We study the problem of learning-to-learn: inferring a learning algorithm that works well on tasks sampled from an unknown distribution.

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

no code implementations2 Mar 2019 Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto

We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs.

Structured Prediction

Learning To Learn Around A Common Mean

no code implementations NeurIPS 2018 Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

We show that, in this setting, the LTL problem can be reformulated as a Least Squares (LS) problem and we exploit a novel meta- algorithm to efficiently solve it.

Meta-Learning

Manifold Structured Prediction

no code implementations NeurIPS 2018 Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure.

Structured Prediction

Localized Structured Prediction

no code implementations NeurIPS 2019 Carlo Ciliberto, Francis Bach, Alessandro Rudi

Key to structured prediction is exploiting the problem structure to simplify the learning process.

Computer Vision Learning Theory +1

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

2 code implementations NeurIPS 2018 Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto

Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization.

Approximating Hamiltonian dynamics with the Nyström method

no code implementations6 Apr 2018 Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.

Incremental Learning-to-Learn with Statistical Guarantees

no code implementations21 Mar 2018 Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution.

Incremental Learning online learning

Low Compute and Fully Parallel Computer Vision With HashMatch

no code implementations ICCV 2017 Sean Ryan Fanello, Julien Valentin, Adarsh Kowdle, Christoph Rhemann, Vladimir Tankovich, Carlo Ciliberto, Philip Davidson, Shahram Izadi

Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.

Computer Vision Disparity Estimation +2

Are we done with object recognition? The iCub robot's perspective

1 code implementation28 Sep 2017 Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale

We report on an extensive study of the benefits and limitations of current deep learning approaches to object recognition in robot vision scenarios, introducing a novel dataset used for our investigation.

Image Retrieval Object Categorization +2

Quantum machine learning: a classical perspective

no code implementations26 Jul 2017 Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.

Reexamining Low Rank Matrix Factorization for Trace Norm Regularization

no code implementations27 Jun 2017 Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which, however, leads to a non-convex problem.

Matrix Completion

Consistent Multitask Learning with Nonlinear Output Relations

no code implementations NeurIPS 2017 Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil

However, in practice assuming the tasks to be linearly related might be restrictive, and allowing for nonlinear structures is a challenge.

Structured Prediction

Incremental Robot Learning of New Objects with Fixed Update Time

1 code implementation17 May 2016 Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta

We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment.

Active Learning General Classification +1

Convex Learning of Multiple Tasks and their Structure

1 code implementation13 Apr 2015 Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco

In this context a fundamental question is how to incorporate the tasks structure in the learning problem. We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches.

Multi-Task Learning

Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?

no code implementations13 Apr 2015 Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale

In this paper we investigate such possibility, while taking further steps in developing a computational vision system to be embedded on a robotic platform, the iCub humanoid robot.

Computer Vision Image Retrieval +1

Learning Multiple Visual Tasks while Discovering their Structure

no code implementations CVPR 2015 Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa

Multi-task learning is a natural approach for computer vision applications that require the simultaneous solution of several distinct but related problems, e. g. object detection, classification, tracking of multiple agents, or denoising, to name a few.

Computer Vision Denoising +4

iCub World: Friendly Robots Help Building Good Vision Data-Sets

no code implementations15 Jun 2013 Sean Ryan Fanello, Carlo Ciliberto, Matteo Santoro, Lorenzo Natale, Giorgio Metta, Lorenzo Rosasco, Francesca Odone

In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform.

Object Recognition

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