7 citations · 17 across the 10 of their papers we have counts for
11 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 Goliński +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…
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…
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng +2
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved chall…
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…
Modern Bayesian Experimental Design
Tom Rainforth, Adam Foster, Desi R Ivanova +1
Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational…
Understanding top-down attention using task-oriented ablation design
Freddie Bickford Smith, Brett D Roads, Xiaoliang Luo +1
Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. This is known to enhance performance in visua…
The perceptual boost of visual attention is task-dependent in naturalistic settings
Freddie Bickford Smith, Xiaoliang Luo, Brett D. Roads +1
Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with…