45 citations · 168 across the 23 of their papers we have counts for
7 papers · 1 filter
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks' target vec…
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning
Giulia Luise, Massimiliano Pontil, Carlo Ciliberto
The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation. While recent attention has been devoted…
Hyperbolic Manifold Regression
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 mode…
Support-weighted Adversarial Imitation Learning
Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori +1
Adversarial Imitation Learning (AIL) is a broad family of imitation learning methods designed to mimic expert behaviors from demonstrations. While AIL has shown state-of-the-art pe…
A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings
Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more…
Structured Prediction for Conditional Meta-Learning
Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
The goal of optimization-based meta-learning is to find a single initialization shared across a distribution of tasks to speed up the process of learning new tasks. Conditional met…