Active Feature Acquisition With Incomplete Training Data

arXiv:2609.32325 · cs.LG, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/Linusaronsson/AFA-Benchmark

Terminology

• Regret — 20× in this paper · explained in 3 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Active Feature Acquisition — 14× in this paper
• Action Space — 10× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Active Feature Acquisition (AFA) — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Feature Selection — 3× in this paper · explained in 4 episodes
• Missing Completely At Random — 3× in this paper
• Generative Models — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Missing At Random — 1× in this paper
• Missing Not At Random — 1× in this paper
• Partially Observable Markov Decision Process — 1× in this paper
• Partially observable Markov decision process (POMDP) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
• A Survey on Active Feature Acquisition Strategies
• Evaluation of Active Feature Acquisition Methods for Static Feature Settings
• Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs

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