Is margin all you need? An extensive empirical study of active learning on tabular data

• URL: http://arxiv.org/abs/2210.03822v1
• Date: Fri, 7 Oct 2022 21:18:24 GMT
• Title: Is margin all you need? An extensive empirical study of active learning on tabular data
• Authors: Dara Bahri, Heinrich Jiang, Tal Schuster, Afshin Rostamizadeh
• Abstract summary: We analyze the performance of a variety of active learning algorithms on 69 real-world datasets from the OpenML-CC18 benchmark. Surprisingly, we find that the classical margin sampling technique matches or outperforms all others, including current state-of-art.
• Score: 66.18464006872345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a labeled training set and a collection of unlabeled data, the goal of active learning (AL) is to identify the best unlabeled points to label. In this comprehensive study, we analyze the performance of a variety of AL algorithms on deep neural networks trained on 69 real-world tabular classification datasets from the OpenML-CC18 benchmark. We consider different data regimes and the effect of self-supervised model pre-training. Surprisingly, we find that the classical margin sampling technique matches or outperforms all others, including current state-of-art, in a wide range of experimental settings. To researchers, we hope to encourage rigorous benchmarking against margin, and to practitioners facing tabular data labeling constraints that hyper-parameter-free margin may often be all they need.

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