27 citations · 96 across the 9 of their papers we have counts for
11 papers
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Benjie Wang, Clare Lyle, Marta Kwiatkowska
Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions…
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez +2
Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural network…
On The Effect of Auxiliary Tasks on Representation Dynamics
Clare Lyle, Mark Rowland, Georg Ostrovski +1
While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achie…
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal +3
We study reinforcement learning (RL) with no-reward demonstrations, a setting in which an RL agent has access to additional data from the interaction of other agents with the same…
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…
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…