no code implementations • 15 Feb 2024 • Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations.
1 code implementation • 22 Dec 2022 • Ruohan Wang, Isak Falk, Massimiliano Pontil, Carlo Ciliberto
Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific.
no code implementations • 11 Oct 2022 • Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto
A continual learning (CL) algorithm learns from a non-stationary data stream.
1 code implementation • 27 May 2022 • Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil
We formalize a framework to learn the Koopman operator from finite data trajectories of the dynamical system.
1 code implementation • 28 Mar 2022 • Dafni Antotsiou, Carlo Ciliberto, Tae-Kyun Kim
This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared.
1 code implementation • 11 Feb 2022 • Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj, Alessandro Rudi
Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms.
1 code implementation • 8 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.
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.
no code implementations • NeurIPS 2021 • Alessandro Rudi, Carlo Ciliberto
Finding a good way to model probability densities is key to probabilistic inference.
no code implementations • 30 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.
1 code implementation • 25 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.
1 code implementation • 25 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.
no code implementations • NeurIPS 2020 • Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints.
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.
no code implementations • 25 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.
no code implementations • 29 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.
no code implementations • 28 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.
no code implementations • 20 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.
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.
no code implementations • 13 Feb 2020 • Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
We propose and analyze a novel theoretical and algorithmic framework for structured prediction.
no code implementations • 28 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.
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.
no code implementations • 25 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.
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.
2 code implementations • 16 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.
1 code implementation • 25 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.
no code implementations • 2 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.
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.
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.
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.
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.
no code implementations • 6 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.
no code implementations • 21 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.
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.
1 code implementation • 28 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.
no code implementations • 26 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.
no code implementations • 27 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.
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.
no code implementations • NeurIPS 2016 • Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco
We propose and analyze a regularization approach for structured prediction problems.
1 code implementation • 17 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.
no code implementations • 23 Sep 2015 • Giulia Pasquale, Tanis Mar, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale
The importance of depth perception in the interactions that humans have within their nearby space is a well established fact.
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.
1 code implementation • 13 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.
no code implementations • 13 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.
no code implementations • CVPR 2014 • Sean Ryan Fanello, Nicoletta Noceti, Carlo Ciliberto, Giorgio Metta, Francesca Odone
In this paper we propose a weighted supervised pooling method for visual recognition systems.
no code implementations • 15 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.