6 citations · 13 across the 8 of their papers we have counts for
8 papers
LoRA ensembles for large language model fine-tuning
Xi Wang, Laurence Aitchison, Maja Rudolph
Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution…
An Improved Variational Approximate Posterior for the Deep Wishart Process
Sebastian Ober, Ben Anson, Edward Milsom +1
Deep kernel processes are a recently introduced class of deep Bayesian models that have the flexibility of neural networks, but work entirely with Gram matrices. They operate by al…
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…
Decision trees compensate for model misspecification
Hugh Panton, Gavin Leech, Laurence Aitchison
The best-performing models in ML are not interpretable. If we can explain why they outperform, we may be able to replicate these mechanisms and obtain both interpretability and per…
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…
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…