论文标题
走向可解释的元学习
Towards explainable meta-learning
论文作者
论文摘要
Meta-Learning是一个旨在发现不同机器学习算法在各种预测任务上执行的领域。这样的知识加快了超参数调整或功能工程。通过使用替代模型，预测任务的各个方面，例如元功能，地标模型E.T.C.用于预测预期的性能。最新的方法专注于寻找最佳的元模型，但没有解释这些不同方面如何促进其性能。但是，要建立新一代的元模型，我们需要更深入地了解元功能对模型可调性的重要性和影响。在本文中，我们提出了为可解释的人工智能（XAI）开发的技术，以检查和从黑盒替代模型中提取知识。据我们所知，这是第一篇论文，它显示了如何使用事后解释性来改善元学习。
Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models various aspects of the predictive task such as meta-features, landmarker models e.t.c. are used to predict the expected performance. State of the art approaches are focused on searching for the best meta-model but do not explain how these different aspects contribute to its performance. However, to build a new generation of meta-models we need a deeper understanding of the importance and effect of meta-features on the model tunability. In this paper, we propose techniques developed for eXplainable Artificial Intelligence (XAI) to examine and extract knowledge from black-box surrogate models. To our knowledge, this is the first paper that shows how post-hoc explainability can be used to improve the meta-learning.
