24 citations · 168 across the 34 of their papers we have counts for
8 papers · 1 filter
Bayesian Low-rank Adaptation for Large Language Models
Adam X. Yang, Maxime Robeyns, Xi Wang +1
Low-rank adaptation (LoRA) has emerged as a new paradigm for cost-efficient fine-tuning of large language models (LLMs). However, fine-tuned LLMs often become overconfident especia…
Massively Parallel Reweighted Wake-Sleep
Thomas Heap, Gavin Leech, Laurence Aitchison
Reweighted wake-sleep (RWS) is a machine learning method for performing Bayesian inference in a very general class of models. RWS draws samples from an underlying approximate p…
Imitating careful experts to avoid catastrophic events
Jack R. P. Hanslope, Laurence Aitchison
RL is increasingly being used to control robotic systems that interact closely with humans. This interaction raises the problem of safe RL: how to ensure that a RL-controlled robot…
Taylor TD-learning
Michele Garibbo, Maxime Robeyns, Laurence Aitchison
Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn a critic. However, TD-learning updates can be high variance. Here, we introduce a model-ba…
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
Adam X. Yang, Laurence Aitchison, Henry B. Moss
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating suc…
Random initialisations performing above chance and how to find them
Frederik Benzing, Simon Schug, Robert Meier +5
Neural networks trained with stochastic gradient descent (SGD) starting from different random initialisations typically find functionally very similar solutions, raising the questi…