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cs.LG2026
Position: Stop Preaching and Start Practising Data Frugality for Responsible Development of AI
Sophia N. Wilson, Andrew Millard, Guðrún Fjóla Guðmundsdóttir +2
This position paper argues that the machine learning community must move from preaching to practising data frugality for responsible artificial intelligence (AI) development. For t…
cs.LG2026
Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
Andrew Millard, Joshua Murphy, Peter Green +1
Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN). Exact posteriors are usually intractable, in which case approxima…
cs.LG2025
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Andrew Millard, Joshua Murphy, Simon Maskell +1
Partial Bayesian neural networks (pBNNs) have been shown to perform competitively with fully Bayesian neural networks while only having a subset of the parameters be stochastic. Us…