Early stopping is a widely-used regularization technique to avoid overfitting in iterative algorithms. In particular, Split Linearized Bregman Iteration methods are often equipped with an early stopping rule to achieve model selection consistency to recover the structural sparsity of parameters. However, theoretical early stopping rule with model selection consistency requires the incoherence condition, which is unknown in applications. In this work, we propose a data adaptive early stopping rule towards the False Discovery Rate (FDR) control under the framework of Knockoff methods. An inflated FDR is proved under a relaxation of the exchangeability condition in traditional Knockoff methods. The effectiveness of the proposed method is demonstrated by both simulations and two real world application examples, Alzheimer’s Disease (AD) and partial order ranking of basketball teams.

5月2日
11:00am - 12:00pm
地点
https://hkust.zoom.us/j/94401529969 (Passcode: hkust)
讲者/表演者
Miss Wenqi ZENG
HKUST
主办单位
Department of Mathematics
联系方法
付款详情
对象
Alumni, Faculty and staff, PG students, UG students
语言
英语
其他活动
3月24日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Pushing the Limit of Nonlinear Vibrational Spectroscopy for Molecular Surfaces/Interfaces Studies
Abstract Surfaces and interfaces are ubiquitous in Nature. Sum-frequency generation vibrational spectroscopy (SFG-VS) is a powerful surface/interface selective and sub-monolayer sensitive spect...
11月22日
研讨会, 演讲, 讲座
IAS / School of Science Joint Lecture - Leveraging Protein Dynamics Memory with Machine Learning to Advance Drug Design: From Antibiotics to Targeted Protein Degradation
Abstract Protein dynamics are fundamental to protein function and encode complex biomolecular mechanisms. Although Markov state models have made it possible to capture long-timescale protein co...