Extensions of regret-minimization algorithm for optimal design

• URL: http://arxiv.org/abs/2503.19874v1
• Date: Tue, 25 Mar 2025 17:37:09 GMT
• Title: Extensions of regret-minimization algorithm for optimal design
• Authors: Youguang Chen, George Biros,
• Abstract summary: We explore extensions and applications of the regret minimization framework introduced bycitedesign for solving optimal experimental design problems.<n>We incorporate the entropy regularizer into this framework, leading to a novel sample selection objective and a provable sample bound complexity.<n>As an application, we use our algorithm to select a small set of representative samples from image classification datasets without relying on label information.
• Score: 3.4245017707416148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore extensions and applications of the regret minimization framework introduced by~\cite{design} for solving optimal experimental design problems. Specifically, we incorporate the entropy regularizer into this framework, leading to a novel sample selection objective and a provable sample complexity bound that guarantees a $(1+\epsilon)$-near optimal solution. We further extend the method to handle regularized optimal design settings. As an application, we use our algorithm to select a small set of representative samples from image classification datasets without relying on label information. To evaluate the quality of the selected samples, we train a logistic regression model and compare performance against several baseline sampling strategies. Experimental results on MNIST, CIFAR-10, and a 50-class subset of ImageNet show that our approach consistently outperforms competing methods in most cases.

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