.png){kind=icon}
返回
Guilt-Free Data Reuse
.png){kind=icon}
OA
AI
DOI：10.1145/3051088.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Existing approaches to ensuring the validity of inferences drawn from data assume a fixed procedure to be performed, selected before the data are examined. Yet the practice of data analysis is an intrinsically interactive and adaptive process: new analyses and hypotheses are proposed after seeing the results of previous ones, parameters are tuned on the basis of obtained results, and datasets are shared and reused. In this work, we initiate a principled study of how to guarantee the validity of statistical inference in adaptive data analysis. We demonstrate new approaches for addressing the challenges of adaptivity that are based on techniques developed in privacy-preserving data analysis. As an application of our techniques we give a simple and practical method for reusing a holdout (or testing) set to validate the accuracy of hypotheses produced adaptively by a learning algorithm operating on a training set.
Keyword:
STABILITY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12.2
论文数:
1.2W
被引数:
3.7W
机构
引用论文
.png){kind=icon}
Preparation and Characterization of Inclusion Complexes of Poly(propylene glycol) with Cyclodextrins
PREAI
分享收藏Low-Latency Multiuser Two-Way Wireless Relaying for Spectral and Energy Efficiencies低延迟多用户双向无线中继，实现频谱和能量效率OAAI
OAAI分享收藏
查看更多内容