mlsanityData to 14 Oct 2026

Evaluation of Active Feature Acquisition Methods for Static Feature Settings

arXiv 2312.03619 ↗First posted 6 Dec 2023stat.ML cs.LGcited by 4

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

Active feature acquisition (AFA) agents, crucial in domains like healthcare where acquiring features is often costly or harmful, determine the optimal set of features for a subsequent classification task. As deploying an AFA agent introduces a shift in missingness distribution, it's vital to assess its expected performance at deployment using retrospective data. In a companion paper, we introduce a semi-offline reinforcement learning (RL) framework for active feature acquisition performance evaluation (AFAPE) where features are assumed to be time-dependent. Here, we study and extend the AFAPE problem to cover static feature settings, where features are time-invariant, and hence provide more flexibility to the AFA agents in deciding the order of the acquisitions. In this static feature setting, we derive and adapt new inverse probability weighting (IPW), direct method (DM), and double reinforcement learning (DRL) estimators within the semi-offline RL framework. These estimators can be applied when the missingness in the retrospective dataset follows a missing-at-random (MAR) pattern. They also can be applied to missing-not-at-random (MNAR) patterns in conjunction with appropriate existing missing data techniques. We illustrate the improved data efficiency offered by the semi-offline RL estimators in synthetic and real-world data experiments under synthetic MAR and MNAR missingness.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 19 of its 56 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

19 of 56 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings3 Dec
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems4 May

Other references · 17

  1. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 133
  2. 2019Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processescited by 119
  3. 2018EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAEcited by 87
  4. 2021What's a good imputation to predict with missing values?cited by 62
  5. 2020Active Feature Acquisition with Generative Surrogate Modelscited by 54
  6. 2020Full Law Identification In Graphical Models Of Missing Data: Completeness Resultscited by 26
  7. 2019Classification with Costly Features as a Sequential Decision-Making Problemcited by 23
  8. 2020Reinforcement Learning with Efficient Active Feature Acquisitioncited by 21
  9. 2019Identification In Missing Data Models Represented By Directed Acyclic Graphscited by 19
  10. 2019ASAC: Active Sensing using Actor-Critic modelscited by 13
  11. 2020Inverse Active Sensing: Modeling and Understanding Timely Decision-Makingcited by 12
  12. 2017Semiparametric theorycited by 7
5 more
  1. 2018An Optimal Policy for Patient Laboratory Tests in Intensive Care Unitscited by 7
  2. 2019Dynamic Measurement Scheduling for Event Forecasting using Deep RLcited by 7
  3. 2020Dynamic Feature Acquisition with Arbitrary Conditional Flowscited by 6
  4. 2019Efficient estimation of optimal regimes under a no direct effect assumptioncited by 5
  5. 2020Pattern graphs: a graphical approach to nonmonotone missing datacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025A Survey on Active Feature Acquisition Strategiescited by 17
  2. 2025Learning-To-Measure: In-Context Active Feature Acquisitioncited by 5
  3. 2026Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modelingcited by 0
  4. 2026Active Feature Acquisition With Incomplete Training Datacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
  2. 2025Information Templates: A New Paradigm for Intelligent Active Feature Acquisition
  3. 2025Stochastic Encodings for Active Feature Acquisition
  4. 2026AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints
  5. 2026Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
  6. 2026Sampling-guided exploration of active feature selection policies
  7. 2022Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task
  8. 2026Quantifying Potential Observation Missingness in Inverse Reinforcement Learning