# Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition

arXiv: https://arxiv.org/abs/2302.13960 · HTML: https://mlsanity.com/paper/2302.13960/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE](https://mlsanity.com/paper/1809.11142/) (2018)
- [Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation](https://mlsanity.com/paper/2008.07146/) (2020)
- [Variational Autoencoder with Arbitrary Conditioning](https://mlsanity.com/paper/1806.02382/) (2018)
- [Active Feature Acquisition with Generative Surrogate Models](https://mlsanity.com/paper/2010.02433/) (2020)
- [Flow Models for Arbitrary Conditional Likelihoods](https://mlsanity.com/paper/1909.06319/) (2019)
- [Transformation Autoregressive Networks](https://mlsanity.com/paper/1801.09819/) (2018)
- [SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks](https://mlsanity.com/paper/1901.03704/) (2019)
- [Survey on Feature Selection](https://mlsanity.com/paper/1510.02892/) (2015)
- [Arbitrary Conditional Distributions with Energy](https://mlsanity.com/paper/2102.04426/) (2021)
- [Learning to Retrieve Videos by Asking Questions](https://mlsanity.com/paper/2205.05739/) (2022)
- [Learning about an exponential amount of conditional distributions](https://mlsanity.com/paper/1902.08401/) (2019)
- [Active Information Acquisition](https://mlsanity.com/paper/1602.02181/) (2016)

## Built on by (most cited first, 19 of 19)

- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Stochastic Encodings for Active Feature Acquisition](https://mlsanity.com/paper/2508.01957/) (2025)
- [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)
- [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)
- [Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients](https://mlsanity.com/paper/2605.05511/) (2026)
- [Relaxed Efficient Acquisition of Context and Temporal Features](https://mlsanity.com/paper/2603.11370/) (2026)
- [Hypernetworks for Dynamic Feature Selection](https://mlsanity.com/paper/2605.12278/) (2026)
- [Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring](https://mlsanity.com/paper/2606.09433/) (2026)
- [Zero-Shot Active Feature Acquisition via LLM-Elicitation](https://mlsanity.com/paper/2606.18933/) (2026)
- [BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition](https://mlsanity.com/paper/2608.02305/) (2026)
- [Learning Source Acquisition Policies by Offline Planning](https://mlsanity.com/paper/2609.14299/) (2026)
- [When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads](https://mlsanity.com/paper/2609.22299/) (2026)
- [RCShift: Certifying When Partial Linkage Suffices for Finite-Sample Decisions](https://mlsanity.com/paper/2609.26207/) (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)
- [Measure Less, Know More: Self-Supervised Test-Time Feature Acquisition](https://mlsanity.com/paper/2610.03454/) (2026)
- [AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints](https://mlsanity.com/paper/2610.07615/) (2026)

## Abstract (arXiv)

We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions. The AFA framework can be useful in a myriad of domains, including health care applications where the cost of acquiring additional features for a patient (in terms of time, money, risk, etc.) can be weighed against the expected improvement to diagnostic performance. Previous approaches for AFA have employed either: deep learning RL techniques, which have difficulty training policies in the AFA MDP due to sparse rewards and a complicated action space; deep learning surrogate generative models, which require modeling complicated multidimensional conditional distributions; or greedy policies, which fail to account for how joint feature acquisitions can be informative together for better predictions. In this work we show that we can bypass many of these challenges with a novel, nonparametric oracle based approach, which we coin the acquisition conditioned oracle (ACO). Extensive experiments show the superiority of the ACO to state-of-the-art AFA methods when acquiring features for both predictions and general decision-making.