Prediction-Oriented Subsampling from Data Streams

Benedetta Lavinia Mussati; Freddie Bickford Smith; Tom Rainforth; Stephen Roberts

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arXiv · 2508.03868

Prediction-Oriented Subsampling from Data Streams

Benedetta Lavinia Mussati·Freddie Bickford Smith·Tom Rainforth·Stephen Roberts

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Abstract

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 computational costs manageable. We explore intelligent data subsampling for offline learning, and argue for an information-theoretic method centred on reducing uncertainty in downstream predictions of interest. Empirically, we demonstrate that this prediction-oriented approach performs better than a previously proposed information-theoretic technique on two widely studied problems. At the same time, we highlight that reliably achieving strong performance in practice requires careful model design.

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Benedetta Lavinia Mussati, Freddie Bickford Smith, Tom Rainforth, Stephen Roberts. 2025-08-05. Prediction-Oriented Subsampling from Data Streams. https://arxiv.org/abs/2508.03868

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