45 citations · 168 across the 20 of their papers we have counts for
12 papers · 1 filter
The Role of Global Labels in Few-Shot Classification and How to Infer Them
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. Recently, feature pre-training has become a ubi…
Conditional Meta-Learning of Linear Representations
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. The effectiveness of these methods is often limited when…
Adversarial Imitation Learning with Trajectorial Augmentation and Correction
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. A way to overcome this shortage of labe…
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…
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…
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…