7 citations · 17 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Making Better Use of Unlabelled Data in Bayesian Active Learning
Freddie Bickford Smith, Adam Foster, Tom Rainforth
Fully supervised models are predominant in Bayesian active learning. We argue that their neglect of the information present in unlabelled data harms not just predictive performance…
Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar +3
Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BA…