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大数据管理与分析方法研究北京市重点实验室（BDAI）研究生沙龙由中国人民大学高瓴人工智能学院师生组织定期举行。本周研讨会由刘勇准聘副教授指导的学生朱博炜介绍自己的研究工作。欢迎同学们积极参与研讨！

报告题目：General Approximate Cross-Validation for Model Selection: Supervised, Semi-supervised and Pairwise Learning

报告人：朱博炜，研究生二年级，导师：刘勇

研究方向：机器学习

摘要：Cross-validation (CV) is a ubiquitous model-agnostic tool for assessing the error of machine learning. However, it has high complexity due to the requirement of multiple times of learner training, especially in multimedia tasks with huge amounts of data. In this paper, we provide a unified framework to approximate the CV error for various common multimedia tasks such as supervised, semi-supervised, and pairwise learning which requires training only once. Moreover, we study the theoretical performance of the proposed approximate CV and provide an explicit finite-sample error bound. Experimental results on several datasets demonstrate that our approximate CV has no statistical discrepancy from the original CV, but can significantly improve the efficiency, which is a great ad- vantage in model selection.