Relaxed Efficient Acquisition of Context and Temporal Features

Yunni Qu; Dzung Dinh; Grant King; Whitney Ringwald; Bing Cai Kok; Kathleen Gates; Aidan Wright; Junier Oliva

cs.LGMar 11, 2026

Relaxed Efficient Acquisition of Context and Temporal Features

Authors: Yunni Qu, Dzung Dinh, Grant King, Whitney Ringwald, Bing Cai Kok, Kathleen Gates, Aidan Wright, Junier Oliva

Organizations: The University of North Carolina at Chapel Hill · University of North Carolina at Chapel Hill, Chapel Hill, NC, USA · University of Michigan · University of Michigan, Ann Arbor, MI, USA ·

Abstract

In many biomedical applications, measurements are not freely available at inference time: each laboratory test, imaging modality, or assessment incurs financial cost, time burden, or patient risk. Longitudinal active feature acquisition (LAFA) seeks to optimize predictive performance under such constraints by adaptively selecting measurements over time, yet the problem remains inherently challenging due to temporally coupled decisions (missed early measurements cannot be revisited, and acquisition choices influence all downstream predictions). Moreover, real-world clinical workflows typically begin with an initial onboarding phase, during which relatively stable contextual descriptors (e.g., demographics or baseline characteristics) are collected once and subsequently condition longitudinal decision-making. Despite its practical importance, the efficient selection of onboarding context has not been studied jointly with temporally adaptive acquisition. We therefore propose REACT (Relaxed Efficient Acquisition of Context and Temporal features), an end-to-end differentiable framework that simultaneously optimizes (i) selection of onboarding contextual descriptors and (ii) adaptive feature--time acquisition plans for longitudinal measurements under cost constraints. REACT employs a Gumbel--Sigmoid relaxation with straight-through estimation to enable gradient-based optimization over discrete acquisition masks, allowing direct backpropagation from prediction loss and acquisition cost. Across real-world longitudinal health and behavioral datasets, REACT achieves improved predictive performance at lower acquisition costs compared to existing longitudinal acquisition baselines, demonstrating the benefit of modeling onboarding and temporally coupled acquisition within a unified optimization framework.

Figures & tables

Figure 1. Overview of REACT. REACT first performs a one-time onboarding acquisition of contextual descriptors at

t=0

. Shaded feature entries denote acquired measurements. Using acquired context and prior longitudinal observations, the planner then adaptively decides both when to acquire next and which features to obtain. Between acquisition steps, the model predicts temporal labels without collecting additional measurements, yielding personalized, nonuniform acquisition trajectories.

Figure 2. Longitudinal planner. Given the onboarding context and longitudinal measurements observed up to time

t

, the planner outputs a binary acquisition mask over future feature–time pairs. The left portion of the mask corresponds to previously acquired measurements (unused), while the right portion specifies the future acquisition plan, judged against the cost-benefit objective. The earliest selected future time defines the next acquisition time,

t_{\mathrm{next}}

Figure 3. AUROC/total cost of models across various average acquisition costs (budgets) on test data. The dashed line is evaluating the pretrained classifier from REACT with all features available.

Figure 4. Example feature acquisition rollouts using REACT for two distinct instances from the (a) CHEEARS and (b) WOMAC test sets. For visual clarity, only the selected features for these instances are displayed.

Panel of Figure 5 — Figure 5. Qualitative visualization of longitudinal acquisition dynamics by REACT across datasets. Metrics are reported per dataset on the test set as (Total/Longitudinal Costs $\mid$ AUROC/AUPRC): ILIADD (35.739/23.993 $\mid$ 0.842/0.706), CHEEARS (13.275/11.275 $\mid$ 0.673/0.540), ADNI (12.635/10.535 $\mid$ 0.823/0.678), WOMAC (30.217/28.141 $\mid$ 0.670/0.355), KLG (29.243/26.869 $\mid$ 0.812/0.621). For visual clarity, only the selected features are displayed, and the stop probabilities are rounded.

Figure 6. Qualitative comparison of acquisition of REACT, DIME, and RAS on the ADNI dataset. Metrics are reported per framework on the test set as (Total/Longitudinal Costs $\mid$ AUROC/AUPRC): REACT (5.335/3.235 $\mid$ 0.824/0.683), DIME (5.619/3.630 $\mid$ 0.644/0.483), RAS (8.675/4.193 $\mid$ 0.817/0.684)

Figure 7. Ablation study of the self-iterative training we used for training REACT (Alg. 1 ). We compare AUROC/total cost when planner $\pi_{\theta}$ trained with (i) self-iterative training from random initialization (Self Training) and (ii) trained using only offline reference states (Offline Reference).

Figure 8. Ablation study on how the learned context selector affects the temporal feature acquisition. AUROC of REACT with learned context descriptors compared to when context descriptors are all acquired (REACT-all) or not at all acquired (REACT-none) across datasets.

Appendix figures & tables15 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9. AUPRC/total cost of models across various average acquisition costs (budgets) on test data. The dashed line is evaluating the pretrained classifier from REACT with all features available.

Figure 10. Ablation study of the self-iterative training for REACT (Alg. 1 ). We compare AUPRC/total cost when planner $\pi_{\theta}$ trained with (i) self-iterative training from random initialization (Self Training) and (ii) trained using only offline reference states (Offline Reference).

Figure 11. Ablation study on how the learned context selector affects the temporal feature acquisition. AUPRC of REACT with learned context descriptors compared to when context descriptors are all acquired (REACT-all) or not at all acquired (REACT-none) across datasets.

Figure 12. Ablation on States Seen During Policy Training.

Dataset	$\lambda$	Total Cost	Temporal Cost	Context Cost
ADNI	0.05	1.100	0.500	0.600
	0.01	4.725	3.225	1.500
	0.0001	5.585	3.485	2.100
CHEEARS	0.003	1.000	0.000	1.000
	0.002	6.851	5.851	1.000
	0.0015	13.275	11.275	2.000

Appendix

Table 1. REACT hyperparameter: acquisition costs and

\lambda

by dataset.

Panel of Figure 13 — Figure 13. Comparison of training and evaluation runtimes.

Feature Name	Acquisition Cost	Description
sex	1.0	Participant sex.
age	1.0	Participant age.
race___4	1.0	Participant race (Black/African American indicator).
hispanic	1.0	Hispanic/Latino ethnicity.
education	1.0	Highest level of education attained.
current_employed	1.0	Current employment status.

Appendix

Table 2. CHEEARS contextual descriptors.

Feature Name	Acquisition Cost	Description
happy	1.0	Self-reported happiness in the past 15 minutes (continuous scale).
nervous	1.0	Self-reported nervousness in the past 15 minutes (continuous scale).
angry	1.0	Self-reported anger in the past 15 minutes (continuous scale).
sad	1.0	Self-reported sadness in the past 15 minutes (continuous scale).
excited	1.0	Self-reported excitement in the past 15 minutes (continuous scale).
alert	1.0	Self-reported alertness in the past 15 minutes (continuous scale).

Appendix

Table 3. CHEEARS temporal features.

Feature Name	Acquisition Cost	Description
age	1.0	Participant age.
sex	1.0	Participant sex.
handedness	1.0	Participant handedness (e.g., left, right, ambidextrous).
multiracial	1.0	Multiracial identity indicator.
hispanic	1.0	Hispanic/Latino ethnicity.
language	1.0	Primary language spoken.

Appendix

Table 4. ILIADD contextual descriptors.

Feature Name	Acquisition Cost	Description
interaction	1.0	Event-contingent report of a social interaction (triggered by participant).
positiveaffEMA	1.0	Self-reported positive affect in the past 15 minutes (0 = Neutral, 10 = Very Positive).
energyEMA	1.0	Self-reported energy/alertness in the past 15 minutes (0 = Not at all, 10 = Very Energetic/Awake).
stressEMA	1.0	Self-reported stress in the past 15 minutes (0 = Not at all, 10 = Extremely).
impulse1EMA	1.0	In the past hour, acted on impulse (0 = Not at all, 10 = Very Much).
impulse2EMA	1.0	In the past hour, did things without worrying about consequences (0 = Not at all, 10 = Very Much).

Appendix

Table 5. ILIADD temporal (EMA) features.

Feature Name	Acquisition Cost	Description
AGE	0.3	Participant age.
PTGENDER	0.3	Participant gender.
PTEDUCAT	0.3	Participant education.
PTETHCAT	0.3	Participant ethnic category.
PTRACCAT	0.3	Participant racial category.
PTMARRY	0.3	Participant marital status.

Appendix

Table 6. ADNI contextual descriptors.

Feature Name	Acquisition Cost	Description
FDG	1.0	FDG PET biomarker.
AV45	1.0	AV45 PET biomarker.
Hippocampus	0.5	Hippocampal MRI biomarker.
Entorhinal	0.5	Entorhinal MRI biomarker.

Appendix

Table 7. ADNI temporal features.

Feature Name	Acquisition Cost	Description
HISP	0.3	Hispanic/Latino ethnicity indicator.
RACE	0.3	Self-reported race category.
SEX	0.3	Participant sex.
FAMHXKR	0.3	Mother, father, sister, or brother had knee repl surgery where all/part of knee replaced.
EDCV	0.3	Highest grade or year of school completed.
AGE	0.3	Age.

Appendix

Table 8. OAI contextual descriptors.

Feature Name	Acquisition Cost	Description
DRNKAMT	0.3	Current alcohol consumption amount.
DRKMORE	0.3	Alcohol-use indicator capturing heavier or more frequent drinking pattern.
BPSYS	0.5	Systolic blood pressure.
BPDIAS	0.5	Diastolic blood pressure.
BMI	0.5	Body mass index.
CEMPLOY	0.3	Current employment.

Appendix

Table 9. OAI temporal features.

Panel of Figure 14 — Figure 14. Selected contexts for the policies shown in Fig. 5 . Bright cells indicate selected features, while dark cells indicate those that were not selected.

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Oct 5, 2026J/K move · Enter open · S save

Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden

Yunni Qu, Bing Cai Kok, Whitney Ringwald, Grant King+3

Department of Computer Science, University of North Carolina at Chapel Hill · Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill · School of Social Sciences, Nanyang Technological University, Singapore+2

Time Series Forecasting Active Learning Active Feature Acquisition Human Behavior Prediction

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Accurate forecasting of pathological outcomes is a central problem in psychology. To do so, psychologists often collect intensive longitudinal data. However, in such studies, the desire to acquire a large number of variables for the sake of accurate prediction is often counteracted by the need to minimize participant burden. Acquiring more variables per occasion can yield better predictions, but having too many acquisitions increase the risk of non-response and attrition. Longitudinal Active Feature Acquisition (LAFA) is a principled approach to resolve this conundrum. Instead of requiring responses to every item at every acquisition occasion, LAFA produces a policy that seeks to optimally select dynamic subsets of items to be acquired at each timepoint while preserving our ability to forecast a specific outcome. However, existing LAFA methods are mostly based on Neural Networks (NN) that are difficult to interpret in practice. In this work, we introduce a tree distillation method for learning an interpretable policy from NN-based LAFA networks. We validated our method through both a simulation and an empirical EMA dataset on forecasting daily alcohol consumption. In both cases, we find that we can meaningfully reduce the number of items acquired at each occasion with minimal loss in accuracy. Networks (NN) that are difficult to interpret in practice. In this work, we introduce a tree distillation method for learning an interpretable policy from NN-based LAFA networks. We validated our method through both a simulation and an empirical EMA dataset on forecasting daily alcohol consumption. In both cases, we find that we can meaningfully reduce the number of items acquired at each occasion with minimal loss in accuracy.

Abstract

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