BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition

The paper introduces BRiG-AFA, a supervised method for non-myopic active feature acquisition that avoids reinforcement learning and conditional-density estimation. It learns a separate candidate-conditioned risk-to-go function for each remaining budget, fitting the functions backward from one-step terminal classification risk using Bellman targets. In a controlled benchmark, BRiG-AFA improves accuracy over a one-step ablation by 4.84±2.17 and 4.39±1.10 percentage points at budgets two and three. On Fashion-MNIST with 20 candidate pixels, the mean paired gain across budgets {2,4,8,12,16} is 3.50±0.37 points, while MiniBooNE results are mixed at small budgets.