7 papers
Active Feature Acquisition With Incomplete Training Data
Reza Rezvan, Valter Schütz, Han Wu +2
In many prediction tasks, acquiring all features can be a prohibitively expensive or outright impossible task. Further, in many cases a static subset of features may not be enough…
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
Linus Aronsson, Morteza Haghir Chehreghani
Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which feature values to acquire for each in…
Cold-Start Active Correlation Clustering
Linus Aronsson, Han Wu, Morteza Haghir Chehreghani
We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we…
AFABench: A Generic Framework for Benchmarking Active Feature Acquisition
Valter Schütz, Han Wu, Reza Rezvan +2
In many real-world scenarios, acquiring all features of a data instance can be expensive or impractical due to monetary cost, latency, or privacy concerns. Active Feature Acquisiti…
A Survey on Active Feature Acquisition Strategies
Linus Aronsson, Arman Rahbar, Morteza Haghir Chehreghani
Active feature acquisition (AFA) studies how to sequentially acquire features for each data instance to trade off predictive performance against acquisition cost. This survey offer…
An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks
Linus Aronsson, Morteza Haghir Chehreghani
Signed networks, where edges are labeled as positive or negative to represent friendly or antagonistic interactions, provide a natural framework for analyzing polarization, trust,…
Information-Theoretic Active Correlation Clustering
Linus Aronsson, Morteza Haghir Chehreghani
Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as i…