1 citations · 1 across the 2 of their papers we have counts for
5 papers
Loss-Driven Bayesian Active Learning
Zhuoyue Huang, Freddie Bickford Smith, Tom Rainforth
The central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acq…
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam GoliÅski +5
We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather information from a user or other external sou…
Prediction-Oriented Subsampling from Data Streams
Benedetta Lavinia Mussati, Freddie Bickford Smith, Tom Rainforth +1
Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping…
Scaling Up Active Testing to Large Language Models
Gabrielle Berrada, Jannik Kossen, Freddie Bickford Smith +3
Active testing enables label-efficient evaluation of predictive models through careful data acquisition, but it can pose a significant computational cost. We identify cost-saving m…
Rethinking Aleatoric and Epistemic Uncertainty
Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope +3
The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discu…