11 citations · 16 across the 7 of their papers we have counts for
4 papers · 1 filter
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Tim Genewein, Li Kevin Wenliang, Jordi Grau-Moya +3
Prompting is one of the main ways to adapt a pretrained model to target tasks. Besides manually constructing prompts, many prompt optimization methods have been proposed in the lit…
Partition Tree Weighting for Non-Stationary Stochastic Bandits
Joel Veness, Marcus Hutter, Andras Gyorgy +1
This paper considers a generalisation of universal source coding for interaction data, namely data streams that have actions interleaved with observations. Our goal will be to cons…
Evaluating Frontier Models for Dangerous Capabilities
Mary Phuong, Matthew Aitchison, Elliot Catt +24
To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evalu…
Learning Universal Predictors
Jordi Grau-Moya, Tim Genewein, Marcus Hutter +8
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile represe…