Extensions of the regret-minimization algorithm for optimal design
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911469026672640 |
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| author | Chen, Youguang Biros, George |
| author_facet | Chen, Youguang Biros, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extensions of the regret-minimization algorithm for optimal design Chen, Youguang Biros, George Machine Learning 62J12, 62L05, 68W27, 68W40, 68T05 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. |
| title | Extensions of the regret-minimization algorithm for optimal design |
| topic | Machine Learning 62J12, 62L05, 68W27, 68W40, 68T05 |
| url | https://arxiv.org/abs/2503.19874 |