检索结果-内蒙古大学图书馆

Testing exchangeability in the batch mode with e-values and Markov alternatives

MACHINE LEARNING 2025年第4期114卷 1-27页

作者： Vovk, Vladimir Royal Holloway Univ London Dept Comp Sci Egham TW20 0EX England

The topic of this paper is testing the assumption of exchangeability, which is the standard assumption in mainstream machine learning. The common approaches are online testing by betting (such as conformal testing) and the older batch testing using p-values (as in classical hypothesis testing). The approach of this paper is intermediate in that we are interested in batch testing by betting;as a result, p-values are replaced by e-values. As a first step in this direction, this paper concentrates on the Markov model as alternative. The null hypothesis of exchangeability is formalized as a Kolmogorov-type compression model, and the Bayes mixture of the Markov model w.r. to the uniform prior is taken as simple alternative hypothesis. Using e-values instead of p-values leads to a computationally efficient testing procedure. Two appendixes discuss connections with the algorithmic theory of randomness;in particular, the test proposed in this paper can be interpreted as a poor man's version of Kolmogorov's deficiency of randomness.

关键词： Testing exchangeability Batch compression models E-values algorithmic theory of randomness

来源：评论

学校读者我要写书评

暂无评论

The Fundamental Nature of the Log Loss Function

引用

Symposium on Logic in Computer Science Celebrating Yuri Gurevich's 75th Birthday (YuriFest)

作者： Vovk, Vladimir Univ London Dept Comp Sci Royal Holloway Egham Surrey England

ISBN: (纸本)9783319235349;9783319235332

The standard loss functions used in the literature on probabilistic prediction are the log loss function, the Brier loss function, and the spherical loss function;however, any computable proper loss function can be used for comparison of prediction algorithms. This note shows that the log loss function is most selective in that any prediction algorithm that is optimal for a given data sequence (in the sense of the algorithmic theory of randomness) under the log loss function will be optimal under any computable proper mixable loss function;on the other hand, there is a data sequence and a prediction algorithm that is optimal for that sequence under either of the two other standard loss functions but not under the log loss function.

关键词： algorithmic theory of randomness Mixability Predictive complexity Predictive randomness Probabilistic prediction Proper loss functions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：