# A Survey on Active Feature Acquisition Strategies

arXiv: https://arxiv.org/abs/2502.11067 · HTML: https://mlsanity.com/paper/2502.11067/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 153 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 30 of 47)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Multimodal Generative Models for Scalable Weakly-Supervised Learning](https://mlsanity.com/paper/1802.05335/) (2018)
- [EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE](https://mlsanity.com/paper/1809.11142/) (2018)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Tutorial on amortized optimization](https://mlsanity.com/paper/2202.00665/) (2022)
- [Variational Autoencoder with Arbitrary Conditioning](https://mlsanity.com/paper/1806.02382/) (2018)
- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [Active Feature Acquisition with Generative Surrogate Models](https://mlsanity.com/paper/2010.02433/) (2020)
- [Near-optimal Regret Bounds for Stochastic Shortest Path](https://mlsanity.com/paper/2002.09869/) (2020)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [Optimal experimental design: Formulations and computations](https://mlsanity.com/paper/2407.16212/) (2024)
- [Classification with Costly Features using Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07364/) (2017)
- [Flow Models for Arbitrary Conditional Likelihoods](https://mlsanity.com/paper/1909.06319/) (2019)
- [Interpretable by Design: Learning Predictors by Composing Interpretable Queries](https://mlsanity.com/paper/2207.00938/) (2022)
- [SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks](https://mlsanity.com/paper/1901.03704/) (2019)
- [Variational Information Pursuit for Interpretable Predictions](https://mlsanity.com/paper/2302.02876/) (2023)
- [Classification with Costly Features as a Sequential Decision-Making Problem](https://mlsanity.com/paper/1909.02564/) (2019)
- [Arbitrary Conditional Distributions with Energy](https://mlsanity.com/paper/2102.04426/) (2021)
- [Reinforcement Learning with Efficient Active Feature Acquisition](https://mlsanity.com/paper/2011.00825/) (2020)
- [Estimating Conditional Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2306.03301/) (2023)
- [Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams](https://mlsanity.com/paper/1901.00243/) (2019)
- [Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition](https://mlsanity.com/paper/2302.13960/) (2023)
- [Adaptive Classification for Prediction Under a Budget](https://mlsanity.com/paper/1705.10194/) (2017)
- [Lower Bounds for Learning in Revealing POMDPs](https://mlsanity.com/paper/2302.01333/) (2023)
- [Pruning Random Forests for Prediction on a Budget](https://mlsanity.com/paper/1606.05060/) (2016)
- [BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis](https://mlsanity.com/paper/2012.01065/) (2020)

## Built on by (most cited first, 17 of 17)

- [AFABench: A Generic Framework for Benchmarking Active Feature Acquisition](https://mlsanity.com/paper/2508.14734/) (2025)
- [Cohort-Based Active Modality Acquisition](https://mlsanity.com/paper/2505.16791/) (2025)
- [The Curious Language Model: Strategic Test-Time Information Acquisition](https://mlsanity.com/paper/2506.09173/) (2025)
- [Information Templates: A New Paradigm for Intelligent Active Feature Acquisition](https://mlsanity.com/paper/2508.18380/) (2025)
- [NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data](https://mlsanity.com/paper/2507.12412/) (2025)
- [When Should Humans Step In? Optimal Human Dispatching in AI-Assisted Decisions](https://mlsanity.com/paper/2603.13688/) (2026)
- [Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients](https://mlsanity.com/paper/2605.05511/) (2026)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [Zero-Shot Active Feature Acquisition via LLM-Elicitation](https://mlsanity.com/paper/2606.18933/) (2026)
- [Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents](https://mlsanity.com/paper/2608.10441/) (2026)
- [Conditional Validity for Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group](https://mlsanity.com/paper/2608.15520/) (2026)
- [Explanations-Driven Active Feature Acquisition for Algorithmic Recourse](https://mlsanity.com/paper/2609.12179/) (2026)
- [When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads](https://mlsanity.com/paper/2609.22299/) (2026)
- [When Does Synergy Help Active Feature Acquisition? A PID-Based Study](https://mlsanity.com/paper/2609.32301/) (2026)
- [Active Feature Acquisition With Incomplete Training Data](https://mlsanity.com/paper/2609.32325/) (2026)
- [Should I stay or should I show? Learning to selectively disclose information](https://mlsanity.com/paper/2609.39818/) (2026)
- [Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition](https://mlsanity.com/paper/2610.03454/) (2026)

## Abstract (arXiv)

Active feature acquisition (AFA) studies how to sequentially acquire features for each data instance to trade off predictive performance against acquisition cost. This survey offers the first unified treatment of AFA via an explicit partially observable Markov decision process (POMDP) formulation. We place this formulation in the broader literature on optimal information acquisition and, more specifically, in a family of structured POMDPs (for example, information-gathering and sensing POMDPs) whose assumptions and algorithmic tools directly apply to AFA. This connection provides a common language for comparing problem settings and methods, and it highlights where AFA can leverage established results in structured POMDP planning and approximation. Building on this perspective, we present an up-to-date taxonomy of AFA methods that (roughly) mirrors standard approaches to solving POMDPs: (i) embedded cost-aware predictors (notably cost-sensitive decision trees and ensembles), (ii) model-based methods that plan using learned probabilistic components, (iii) model-free methods that learn acquisition policies from simulated episodes, and (iv) hybrid methods that combine the strengths of model-based and model-free approaches. We argue that this POMDP-centric view clarifies connections among existing methods and motivates more principled algorithm design. Since much prior work is heuristic and lacks formal guarantees, we also outline routes to guarantees by connecting AFA to adaptive stochastic optimization. We conclude by highlighting open challenges and promising directions for future research.