.png){kind=icon}
Return
Robust Information Selection for Hypothesis Testing with Misclassification Penalties
.png){kind=icon}
PRE
AI
DOI：10.1109/tac.2026.3729441.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We study the problem of robust information selection for a Bayesian hypothesis testing/classification task, where the goal is to identify the true state of the world from a finite set of hypotheses based on observations from the selected information sources. We introduce a novel misclassification penalty framework, which enables non-uniform treatment of different misclassification events. Extending the classical subset selection framework, we study the problem of selecting a subset of sources that minimize the maximum penalty of misclassification under a limited budget, despite deletions or failures of a subset of the selected sources. We characterize the curvature properties of the objective function and propose an efficient greedy algorithm with performance guarantees. Next, we highlight certain limitations of optimizing for the maximum penalty metric and propose an alternate metric with favoruable properties to guide the selection of the information set. We propose a robust greedy algorithm with near-optimality guarantees for optimizing the alternate metric. Finally, we empirically demonstrate the performance of our proposed algorithms in several problem instances.
Keywords:
Information Selection
Adversarial Robustness
Non-Submodular Optimization
Greedy Algorithms
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
Cited Papers
No cited papers available