The idea of federated learning is to collaboratively train a neural network on a server. Each
user receives the current weights of the network and in turns sends parameter updates …

We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …

It is widely believed that the implicit regularization of SGD is fundamental to the impressive
generalization behavior we observe in neural networks. In this work, we demonstrate that …

Meta-learning algorithms produce feature extractors which achieve state-of-the-art
performance on few-shot classification. While the literature is rich with meta-learning …

State-of-the-art federated learning methods can perform far worse than their centralized
counterparts when clients have dissimilar data distributions. For neural networks, even when …

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their
inception. Today, AI-Robotics systems have become an integral part of our daily lives, from …

Fine-tuning from a collection of models pre-trained on different domains (a" model zoo") is
emerging as a technique to improve test accuracy in the low-data regime. However, model …

We challenge the longstanding assumption that the mean-field approximation for variational
inference in Bayesian neural networks is severely restrictive, and show this is not the case in …