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stat.ME2026
Quantifying uncertainty and stability among highly correlated predictors: a subspace perspective
Xiaozhu Zhang, Jacob Bien, Armeen Taeb
We study the problem of linear feature selection when features are highly correlated. Such settings pose two fundamental challenges. First, how should model similarity be defined?…
stat.ME2026
Hierarchical Clustering With Confidence
Di Wu, Jacob Bien, Snigdha Panigrahi
Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However, its greedy nature makes i…
stat.ME2025
Reluctant Interaction Inference after Additive Modeling
Yiling Huang, Snigdha Panigrahi, Guo Yu +1
Additive models enjoy the flexibility of nonlinear models while still being readily understandable to humans. By contrast, other nonlinear models, which involve interactions betwee…