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首页> 外文会议>Annual Conference on Neural Information Processing Systems >Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations
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Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations

机译：使用Kolmogorov复杂性进行转移学习：基础理论和实证评估

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In transfer learning we aim to solve new problems using fewer examples using information gained from solving related problems. Transfer learning has been successful in practice, and extensive PAC analysis of these methods has been developed. However it is not yet clear how to define relatedness between tasks. This is considered as a major problem as it is conceptually troubling and it makes it unclear how much information to transfer and when and how to transfer it. In this paper we propose to measure the amount of information one task contains about another using conditional Kolmogorov complexity between the tasks. We show how existing theory neatly solves the problem of measuring relatedness and transferring the 'right' amount of information in sequential transfer learning in a Bayesian setting. The theory also suggests that, in a very formal and precise sense, no other reasonable transfer method can do much better than our Kolmogorov Complexity theoretic transfer method, and that sequential transfer is always justified. We also develop a practical approximation to the method and use it to transfer information between 8 arbitrarily chosen databases from the UCI ML repository.
机译：在迁移学习中，我们旨在使用解决相关问题获得的信息，以更少的示例来解决新问题。转移学习在实践中已经成功，并且已经开发了对这些方法的广泛PAC分析。但是，尚不清楚如何定义任务之间的相关性。这被认为是一个主要问题，因为它在概念上很麻烦，并且使得不清楚要传输多少信息以及何时以及如何进行传输。在本文中，我们建议使用任务之间的条件Kolmogorov复杂度来衡量一个任务包含的有关另一任务的信息量。我们展示了现有理论如何巧妙地解决在贝叶斯环境下的顺序转移学习中测量关联性和转移“正确”信息量的问题。该理论还表明，在非常正式和精确的意义上，没有其他合理的转移方法可以比我们的Kolmogorov复杂度理论转移方法做得更好，并且顺序转移始终是合理的。我们还为该方法开发了一种实用的近似方法，并使用它在UCI ML存储库中的8个任意选择的数据库之间传输信息。

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