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Selective Conformal Inference with FCR Control

作者: 发布时间:2024-09-03 点击数:
主讲人:任好洁
主讲人简介:

任好洁是上海交通大学数学科学学院长聘教轨副教授,18年博士毕业于南开大学,随后在宾州州立大学从事博士后研究。她的研究方向包括预测推断、统计异常探查、在线学习与监控、高维数据推断等。在JASA,Biometrika等杂志和机器学习顶会ICML,NeurIPS上发表多篇学术论文。

主持人:刘婧媛
讲座简介:

In this talk, we explore recent developments in post-selection/ selective conformal prediction, focusing on the challenge of controlling the false coverage-statement rate (FCR). Conformal inference is a well-established tool for constructing prediction intervals, but its application becomes complex when prediction intervals are selectively reported. 

We first propose a novel framework in offline scenarios, where prediction intervals are constructed only for selected individuals from unlabelled test data. We discuss the limitations of traditional FCR-adjusted methods, which, while controlling FCR, lead to inflated prediction intervals. To address this, we introduce SCOP (Selective Conditional Conformal Prediction), a new approach that utilizes the selection process on both calibration and test sets to achieve more precise prediction intervals while maintaining rigorous FCR control under both exchangeable and non-exchangeable selection rules.
Building on this, we extend our discussion to the online setting, where selection decisions and prediction intervals must be made in real-time. We present a general algorithm CAP (Calibration after Adaptive Pick), which adaptively selects and calibrates based on historical data, providing robust, real-time FCR control even under distribution shifts.
Through a combination of theoretical insights and empirical results, we demonstrate how these advancements enable more accurate and reliable prediction intervals across various settings. We conclude the talk by discussing some related works of selective prediction inference.
 
时间:2024-09-09 (Monday) 16:40-18:00
地点:经济楼N302
讲座语言:中文
主办单位:永利集团3044官网欢迎您、王亚南经济研究院、邹至庄经济研究院
承办单位:
期数:
联系人信息:周梦娜:2182886,zmn1994@xmu.edu.cn
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