495 citations · 721 across the 24 of their papers we have counts for
5 papers · 1 filter
A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle, Lisa Schut, Binxin Ru +2
We take a Bayesian perspective to illustrate a connection between training speed and the marginal likelihood in linear models. This provides two major insights: first, that a measu…
Speedy Performance Estimation for Neural Architecture Search
Binxin Ru, Clare Lyle, Lisa Schut +3
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS). Traditional approaches fac…
On the Benefits of Invariance in Neural Networks
Clare Lyle, Mark van der Wilk, Marta Kwiatkowska +2
Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in de…
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
Andreas Kirsch, Clare Lyle, Yarin Gal
The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models…
Invariant Causal Prediction for Block MDPs
Amy Zhang, Clare Lyle, Shagun Sodhani +5
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. In this paper, we consider the problem o…