54 citations · 77 across the 5 of their papers we have counts for
5 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…
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…
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Amadori +1
We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's r…
MAGAN: Margin Adaptation for Generative Adversarial Networks
Ruohan Wang, Antoine Cully, Hyung Jin Chang +1
We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adapti…