Loss-Driven Bayesian Active Learning

2026-04-13

AI Summary

This work addresses the limitation of existing active learning methods in adapting to diverse loss functions associated with downstream tasks, which hinders optimal data acquisition for final model performance. The authors propose a loss-driven Bayesian active learning framework that establishes, for the first time, a rigorous correspondence between arbitrary loss functions and active learning objectives, directly embedding the loss into the sample selection criterion. Specifically, for losses belonging to the class of weighted Bregman divergences, they derive closed-form expressions for key quantities, enabling efficient optimization. Experimental results demonstrate that the proposed method significantly reduces test loss across a variety of regression and classification tasks, confirming its effectiveness and strong generalization capability.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

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.

Problem

• active learning
• Bayesian
• loss function
• data acquisition
• Bregman divergence

Innovation

• loss-driven active learning
• Bayesian active learning
• Bregman divergence
• data acquisition
• predictive performance