37 citations · 60 across the 7 of their papers we have counts for
7 papers
Embedding Adaptation is Still Needed for Few-Shot Learning
Sébastien M. R. Arnold, Fei Sha
Constructing new and more challenging tasksets is a fruitful methodology to analyse and understand few-shot classification methods. Unfortunately, existing approaches to building t…
learn2learn: A Library for Meta-Learning Research
Sébastien M. R. Arnold, Praateek Mahajan, Debajyoti Datta +2
Meta-learning researchers face two fundamental issues in their empirical work: prototyping and reproducibility. Researchers are prone to make mistakes when prototyping new algorith…
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator
James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei +1
We study the variance of the REINFORCE policy gradient estimator in environments with continuous state and action spaces, linear dynamics, quadratic cost, and Gaussian noise. These…
When MAML Can Adapt Fast and How to Assist When It Cannot
Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha
Model-Agnostic Meta-Learning (MAML) and its variants have achieved success in meta-learning tasks on many datasets and settings. On the other hand, we have just started to understa…
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad +2
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there…
Shapechanger: Environments for Transfer Learning
Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart +1
We present Shapechanger, a library for transfer reinforcement learning specifically designed for robotic tasks. We consider three types of knowledge transfer---from simulation to s…