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Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

机译：通过基于变异的自标注扰动加权多数票的域自适应

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摘要

In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information. We focus on learning classification models defined as a weighted majority vote over a set of real-valued functions. In this context, Germain et al.have shown that a measure of disagreement between these functions is crucial to control. The core of this measure is a theoretical bound-the C-bound -which involves the disagreement and leads to a well performing majority vote learning algorithm in usual non-adaptative supervised setting: MinCq. In this work, we propose a framework to extend MinCq to a domain adaptation scenario. This procedure takes advantage of the recent perturbed variation divergence between distributions proposed by Harel and Mannor. Justified by a theoretical bound on the target risk of the vote, we provide to MinCq a target sample labeled thanks to a perturbed variation-based self-labeling focused on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hyperparameters. Finally, our framework called PV-MinCq shows very promising results on a rotation and translation synthetic problem.
机译：在机器学习中，当从不同分布生成测试（目标）和火车（源）数据时，领域适应问题就到了。因此，一个关键的应用问题是算法设计，该算法能够推广到新的发行版中，而我们没有标签信息。我们专注于学习分类模型，该模型定义为对一组实值函数的加权多数投票。在这种情况下，Germain等人已经表明，衡量这些功能之间的分歧对于控制至关重要。该措施的核心是理论界-C界-涉及分歧，并导致在通常的非自适应监督环境下MinCq表现良好的多数投票学习算法。在这项工作中，我们提出了将MinCq扩展到域适应方案的框架。该程序利用了Harel和Mannor提出的分布之间最近的扰动变化差异。由于对投票目标风险有理论上的限制，我们向MinCq提供了标有目标样本，这要归功于基于扰动的基于变异的自标记，重点是源边际和目标边际看起来相似的区域。我们还研究了自标记的影响，由此得出了调整超参数的原始过程。最后，我们的名为PV-MinCq的框架在旋转和平移综合问题上显示了非常有希望的结果。

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