# Extensions of the regret-minimization algorithm for optimal design

Source: [Extensions of the regret-minimization algorithm for optimal design](<https://www.sota2.com/research/paper/68fa779f1e209131cea746e9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-28T05:00:04.461Z

Authors: Youguang Chen, George Biros

Published: 2025-03-25T17:37:09.000Z

## Abstract

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 $(1+\\epsilon)$-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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.19874>)

[PDF](<https://arxiv.org/pdf/2503.19874.pdf>)