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12/22/2022
Robust Meta-Representation Learning via Global Label Inference and Classification
Few-shot learning (FSL) is a central problem in meta-learning, where lea...
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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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03/28/2022
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division
Deep imitation learning requires many expert demonstrations, which can b...
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02/11/2022
Measuring dissimilarity with diffeomorphism invariance
Measures of similarity (or dissimilarity) are a key ingredient to many m...
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02/08/2022
Distribution Regression with Sliced Wasserstein Kernels
The problem of learning functions over spaces of probabilities - or dist...
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08/09/2021
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Few-shot learning (FSL) is a central problem in meta-learning, where lea...
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06/30/2021
PSD Representations for Effective Probability Models
Finding a good way to model probability densities is key to probabilisti...
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03/30/2021
Conditional Meta-Learning of Linear Representations
Standard meta-learning for representation learning aims to find a common...
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03/25/2021
Adversarial Imitation Learning with Trajectorial Augmentation and Correction
Deep Imitation Learning requires a large number of expert demonstrations...
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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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08/25/2020
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Biased regularization and fine-tuning are two recent meta-learning appro...
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07/29/2020
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
The Generative Adversarial Networks (GAN) framework is a well-establishe...
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05/28/2020
Hyperbolic Manifold Regression
Geometric representation learning has recently shown great promise in se...
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02/20/2020
Support-weighted Adversarial Imitation Learning
Adversarial Imitation Learning (AIL) is a broad family of imitation lear...
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02/20/2020
A Structured Prediction Approach for Conditional Meta-Learning
Optimization-based meta-learning algorithms are a powerful class of meth...
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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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01/28/2020
Fast quantum learning with statistical guarantees
Within the framework of statistical learning theory it is possible to bo...
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05/30/2019
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm
We present a novel algorithm to estimate the barycenter of arbitrary pro...
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05/16/2019
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
We consider the problem of imitation learning from a finite set of exper...
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03/25/2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
We study the problem of learning-to-learn: inferring a learning algorith...
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03/02/2019
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
We study the interplay between surrogate methods for structured predicti...
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06/26/2018
Manifold Structured Prediction
Structured prediction provides a general framework to deal with supervis...
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06/06/2018
Localized Structured Prediction
Key to structured prediction is exploiting the problem structure to simp...
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05/30/2018
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Applications of optimal transport have recently gained remarkable attent...
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04/06/2018
Approximating Hamiltonian dynamics with the Nyström method
Simulating the time-evolution of quantum mechanical systems is BQP-hard ...
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03/21/2018
Incremental Learning-to-Learn with Statistical Guarantees
In learning-to-learn the goal is to infer a learning algorithm that work...
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09/28/2017
Are we Done with Object Recognition? The iCub robot's Perspective
We report on an extensive study of the current benefits and limitations ...
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07/26/2017
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well a...
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06/27/2017
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization
Trace norm regularization is a widely used approach for learning low ran...
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05/23/2017
Consistent Multitask Learning with Nonlinear Output Relations
Key to multitask learning is exploiting relationships between different ...
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05/24/2016
A Consistent Regularization Approach for Structured Prediction
We propose and analyze a regularization approach for structured predicti...
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05/17/2016
Incremental Robot Learning of New Objects with Fixed Update Time
We consider object recognition in the context of lifelong learning, wher...
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04/13/2015
Real-world Object Recognition with Off-the-shelf Deep Conv Nets: How Many Objects can iCub Learn?
The ability to visually recognize objects is a fundamental skill for rob...
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04/13/2015
Learning Multiple Visual Tasks while Discovering their Structure
Multi-task learning is a natural approach for computer vision applicatio...
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06/15/2013
