On the Role of Data in PAC-Bayes Bounds

We prove that linear PAC-Bayes bounds based on choosing the prior as the expected posterior can be improved by conditioning on a subset of the data, even with full knowledge of the underlying distribution. We apply this theoretical insight to achieve state-of-the-art, non-vacuous PAC-Bayes bounds for neural network image classifiers trained via stochastic gradient descent.

Unsupervised Curricula for Visual Meta-Reinforcement Learning

We develop an algorithm that constructs a task distribution for an unsupervised meta-learner by modeling interaction in a visual environment. The task distribution adapts as the agent explores the environment and learns to learn.

Unsupervised Learning via Meta-Learning

We propose CACTUs, a simple unsupervised learning → clustering → meta-learning pipeline for image classification pre-training. CACTUs can be thought of as a method that enables unsupervised meta-learning.

Lazy Abstraction-Based Controller Synthesis

This paper gives a self-contained presentation of lazy, multi-layered ABCS for reachability and safety specifications. It subsumes Multi-Layered Abstraction-Based Controller Synthesis for Continuous-Time Systems and Lazy Abstraction-Based Control for Safety Specifications.

Lazy Abstraction-Based Control for Safety Specifications

In this work, we restrict our attention to safety specifications, and extend multi-layered ABCS to be lazy: we entwine abstraction and synthesis to enable on-demand construction of the abstractions.

Multi-Layered Abstraction-Based Controller Synthesis for Continuous-Time Systems

We generalize abstraction-based controller synthesis (ABCS) and present multi-layered ABCS: we construct multiple abstractions of the system at varying spatiotemporal granularity, and do synthesis by adaptively switching between these layers.

Germanium Wrap-Around Photodetectors on Silicon Photonics

We present a new photodetector architecture in which we wrap the germanium diode around the silicon waveguide on four faces.