Loss-driven Bayesian active learning

Conference item

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 acquisition to different downstream problems and losses. We propose a rigorous loss-driven approach to Bayesian active learning that allows data acquisition to directly target the loss associated with a given decision problem. In particular, we show how any loss can be used to derive a unique objective for optimal data acquisition. Critically, we then show that any loss taking the form of a weighted Bregman divergence permits analytic computation of a central component of its corresponding objective, making the approach applicable in practice. In regression and classification experiments with a range of different losses, we find our approach reduces test losses relative to existing techniques.

Authors: Zhuoyue, H; Bickford Smith, F; Rainforth, TWG

• Publication status: Published
• Peer review status: Peer reviewed
• Host title: Proceedings of the 29th International Conference on Artificial Intelligence and Statistics (AISTATS)
• Series: PMLR
• Series number: 300
• Publication date: 2026-05-02
• Acceptance date: 2026-04-01
• Event title: 29th International Conference on Artificial Intelligence and Statistics (AISTATS) 2026
• Event location: Tangier, Morocco.
• Event website: https://virtual.aistats.org/Conferences/2026
• Event start date: 2026-05-02
• Event end date: 2026-05-05
• ISSN: 2640-3498
• Language: English