Active Feature Acquisition

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints
Oct 6, 2026AbdAlRahman Odeh, Teng-Hui Huang, Hesham El GamalActive Learning Active Feature Acquisition
The University of Sydney School of Electrical and Computer Engineering Sydney 2006, NSW, Australia · The University of Sydney Faculty of Engineering Sydney 2006, NSW, Australia
Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
Oct 5, 2026Yunni Qu, Bing Cai Kok, Whitney Ringwald +4Time Series Forecasting Active Learning
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
Evaluation of Active Feature Acquisition Policies with Tabular Foundation Models
Oct 5, 2026Yuta Kobayashi, Divyam Madaan, Shalmali JoshiOff-Policy Evaluation Value Function Estimation
Department of Biomedical Informatics, Columbia University
HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition
Sep 16, 2026Kamilia Zaripova, Nassir Navab, Azade Farshad +1Rare Disease Diagnosis Clinical Decision Support
Technical University of Munich, Munich, Germany · Computational Health Center, Helmholtz Center Munich, Oberschleißheim, Germany · Munich Center for Machine Learning (MCML), Munich, Germany +2
Explanations-Driven Active Feature Acquisition for Algorithmic Recourse
Sep 14, 2026Vinura Galwaduge, Jagath SamarabanduCounterfactual Explanations Cost-Aware Inference
Dept. of Electrical and Computer Engineering Western University London, ON, Canada
BRiG-AFA: Bellman Risk-to-Go Learning for Non-Myopic Active Feature Acquisition
Aug 3, 2026Jiaorong Feng, Qian Li, Ying LiCost-Aware Inference Active Learning
Curtin Business School, Curtin University, Perth, Western Australia, Australia · †Present affiliation: Independent Researcher. · School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia
Zero-Shot Active Feature Acquisition via LLM-Elicitation
Jun 17, 2026Binyamin Perets, Natalie Mendelson, Shiran Vainberg +3Zero-Shot Learning Markov Random Fields
Faculty of Medicine, Technion. · NVIDIA
Order-Agnostic Autoregressive Modelling with Missing Data
May 7, 2026Ignacio Peis, Pablo M. Olmos, Jes FrellsenIncomplete Data Imputation Learning with Missing Data
Technical University of Denmark & Pioneer Centre for AI · Universidad Carlos III de Madrid
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
May 6, 2026Linus Aronsson, Morteza Haghir ChehreghaniPolicy Gradient Active Learning
Department of Computer Science and Engineering · Chalmers University of Technology & University of Gothenburg · Gothenburg, Sweden

Oct 6, 2026, cs.LGJ/K move · Enter open · S save

AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints

AbdAlRahman Odeh, Teng-Hui Huang, Hesham El Gamal

The University of Sydney School of Electrical and Computer Engineering Sydney 2006, NSW, Australia · The University of Sydney Faculty of Engineering Sydney 2006, NSW, Australia

Active Learning Active Feature Acquisition Combinatorial Multi-Armed Bandits

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Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's label. Unlike batch AFA, which trains a fixed policy and classifier offline on fully observed data, online AFA updates its predictor from revealed labels as samples arrive. Existing online methods either use deep reinforcement learning (RL) without performance guarantees or maximize cost-adjusted reward rather than enforce a global budget. We formulate online AFA as a combinatorial Bandits with Knapsacks (BwK) problem that couples acquisition and prediction. Unlike prior bandit-based AFA and classical BwK, our setting has combinatorial complexity, evolving rewards, a global budget, and structured side information. We obtain an improved regret upper bound over standard BwK bounds in this framework, leveraging a cardinality-aware confidence bound and the subset update structure. To avoid an exponentially large action space, we propose \emph{LP-Chain}, a variant that searches a cost-aware chain of feature subsets with a size that grows linearly with the number of features. While the regret upper bound is specific to the combinatorial framework, \emph{LP-Chain} empirically achieves comparable predictive performance. On synthetic data, \emph{LP-Chain} outperforms HEDGE-based BwK and deep RL-based online AFA baselines and scales favorably to more features.

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