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Found 46 papers, 17 papers with code
Closed-form Filtering for Non-linear Systems
Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations.
Robust Meta-Representation Learning via Global Label Inference and Classification
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.
Schedule-Robust Online Continual Learning
A continual learning (CL) algorithm learns from a non-stationary data stream.
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces
We formalize a framework to learn the Koopman operator from finite data trajectories of the dynamical system.
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division
This is done by using proto-policies as modules to divide the tasks into simple sub-behaviours that can be shared.
Measuring dissimilarity with diffeomorphism invariance
Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms.
Distribution Regression with Sliced Wasserstein Kernels
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
Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data.
PSD Representations for Effective Probability Models
Finding a good way to model probability densities is key to probabilistic inference.
Conditional Meta-Learning of Linear Representations
Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks.
Adversarial Imitation Learning with Trajectorial Augmentation and Correction
Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks.
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
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.
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
One way to reach this goal is by modifying the data representation in order to meet certain fairness constraints.
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning
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.
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
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.
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation.
Hyperbolic Manifold Regression
Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models.
Structured Prediction for Conditional Meta-Learning
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.
Support-weighted Adversarial Imitation Learning
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.
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
We propose and analyze a novel theoretical and algorithmic framework for structured prediction.
Statistical Limits of Supervised Quantum Learning
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.
Online-Within-Online Meta-Learning
We study the problem of learning a series of tasks in a fully online Meta-Learning setting.
Support-guided Adversarial Imitation Learning
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.
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
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
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals.
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
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
We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs.
Learning To Learn Around A Common Mean
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.
Manifold Structured Prediction
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure.
Localized Structured Prediction
Key to structured prediction is exploiting the problem structure to simplify the learning process.
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization.
Approximating Hamiltonian dynamics with the Nyström method
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
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.
Low Compute and Fully Parallel Computer Vision With HashMatch
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.
Are we done with object recognition? The iCub robot's perspective
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.
Quantum machine learning: a classical perspective
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
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.
Consistent Multitask Learning with Nonlinear Output Relations
However, in practice assuming the tasks to be linearly related might be restrictive, and allowing for nonlinear structures is a challenge.
A Consistent Regularization Approach for Structured Prediction
We propose and analyze a regularization approach for structured prediction problems.
Incremental Robot Learning of New Objects with Fixed Update Time
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.
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
The importance of depth perception in the interactions that humans have within their nearby space is a well established fact.
Learning Multiple Visual Tasks while Discovering their Structure
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.
Convex Learning of Multiple Tasks and their Structure
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.
Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?
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.
Ask the Image: Supervised Pooling to Preserve Feature Locality
In this paper we propose a weighted supervised pooling method for visual recognition systems.
iCub World: Friendly Robots Help Building Good Vision Data-Sets
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.
