24 citations · 168 across the 34 of their papers we have counts for
7 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…
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…
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…