# Stochastic Encodings for Active Feature Acquisition

arXiv: https://arxiv.org/abs/2508.01957 · HTML: https://mlsanity.com/paper/2508.01957/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 77 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 29 of 29)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Deep Reinforcement Learning and the Deadly Triad](https://mlsanity.com/paper/1812.02648/) (2018)
- [EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE](https://mlsanity.com/paper/1809.11142/) (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)
- [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)
- [Variational Information Pursuit for Interpretable Predictions](https://mlsanity.com/paper/2302.02876/) (2023)
- [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)
- [Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo](https://mlsanity.com/paper/2202.04599/) (2022)
- [Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition](https://mlsanity.com/paper/2302.13960/) (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)
- [Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task](https://mlsanity.com/paper/2211.05039/) (2022)
- [A Probabilistic Hard Attention Model For Sequentially Observed Scenes](https://mlsanity.com/paper/2111.07534/) (2021)
- [Active Information Acquisition](https://mlsanity.com/paper/1602.02181/) (2016)
- [Learning Sparse Latent Representations with the Deep Copula Information Bottleneck](https://mlsanity.com/paper/1804.06216/) (2018)
- [Feature-Budgeted Random Forest](https://mlsanity.com/paper/1502.05925/) (2015)
- [Dynamic Feature Acquisition Using Denoising Autoencoders](https://mlsanity.com/paper/1811.01249/) (2018)
- [Dynamic Feature Acquisition with Arbitrary Conditional Flows](https://mlsanity.com/paper/2006.07701/) (2020)

## Built on by (most cited first, 9 of 9)

- [Learning-To-Measure: In-Context Active Feature Acquisition](https://mlsanity.com/paper/2510.12624/) (2025)
- [Information Templates: A New Paradigm for Intelligent Active Feature Acquisition](https://mlsanity.com/paper/2508.18380/) (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)
- [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)
- [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)

## Abstract (arXiv)

Active Feature Acquisition is an instance-wise, sequential decision making problem. The aim is to dynamically select which feature to measure based on current observations, independently for each test instance. Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic acquisitions. To address these shortcomings, we introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a stochastic latent space. Extensive evaluation on a large range of synthetic and real datasets demonstrates that our approach reliably outperforms a diverse set of baselines.