8 citations · 15 across the 4 of their papers we have counts for
4 papers
Gold after Randomized Sand: Model-X Split Knockoffs for Controlled Transformation Selection
Yang Cao, Hangyu Lin, Xinwei Sun +1
Controlling the False Discovery Rate (FDR) is critical for reproducible variable selection, especially given the prevalence of complex predictive modeling. The recent Split Knockof…
Split Knockoffs for Multiple Comparisons: Controlling the Directional False Discovery Rate
Yang Cao, Xinwei Sun, Yuan Yao
Multiple comparisons in hypothesis testing often encounter structural constraints in various applications. For instance, in structural Magnetic Resonance Imaging for Alzheimer's Di…
Detecting Change Intervals with Isolation Distributional Kernel
Yang Cao, Ye Zhu, Kai Ming Ting +4
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have…
Controlling the False Discovery Rate in Transformational Sparsity: Split Knockoffs
Yang Cao, Xinwei Sun, Yuan Yao
Controlling the False Discovery Rate (FDR) in a variable selection procedure is critical for reproducible discoveries, and it has been extensively studied in sparse linear models.…