Loss-Driven Bayesian Active Learning
For practitioners using Bayesian active learning, this work provides a principled way to tailor data acquisition to specific downstream losses, offering flexibility beyond standard methods.
This paper proposes a loss-driven Bayesian active learning framework that enables data acquisition to directly target the loss of a given decision problem. In experiments, the approach reduces test losses compared to existing techniques across various regression and classification tasks.
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.