73 citations · 166 across the 8 of their papers we have counts for
12 papers
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng +6
Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable…
In-context Reinforcement Learning with Algorithm Distillation
Michael Laskin, Luyu Wang, Junhyuk Oh +11
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal seque…
Self-Consistent Models and Values
Gregory Farquhar, Kate Baumli, Zita Marinho +4
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment. In particular, models enable planning,…
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…
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister +3
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta, Angelos Filos, Yarin Gal +1
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertai…