改进后悔最小化算法，更高效地选样本训练分类器。
Extensions of the regret-minimization algorithm for optimal design
- 用新正则化方案优化样本选择目标
- 理论保证1+ε近似解，样本复杂度更低
- 适用于多类分类与岭回归，适合小样本学习
本文研究从n个无标签样本中选择子集进行标注，以训练多分类器的问题。基于Allen-Zhu等人在ICML 2017提出的后悔最小化框架，我们提出一种新的正则化策略，导出新的样本选择目标，并给出可证明的样本复杂度界，保证获得(1+ε)近似解。此外，我们将该方法扩展至岭回归设置中的实验设计。在MNIST、CIFAR-10及一个50类ImageNet子集上，使用逻辑回归评估所选样本，结果表明，本方法在多数场景下均显著优于现有先进方法。
原文摘要 · Abstract (English)
We consider the problem of selecting a subset of points from a dataset of $n$ unlabeled examples for labeling, with the goal of training a multiclass classifier. To address this, we build upon the regret minimization framework introduced by Allen-Zhu et al. in "Near-optimal design of experiments via regret minimization" (ICML, 2017). We propose an alternative regularization scheme within this framework, which leads to a new sample selection objective along with a provable sample complexity bound that guarantees a $(1+ε)$-approximate solution. Additionally, we extend the regret minimization approach to handle experimental design in the ridge regression setting. We evaluate the selected samples using logistic regression and compare performance against several state-of-the-art methods. Our empirical results on MNIST, CIFAR-10, and a 50-class subset of ImageNet demonstrate that our method consistently outperforms competing approaches across most scenarios.
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