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Found 5 papers, 0 papers with code
Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms
The training-conditional coverage performance of the conformal prediction is known to be empirically sound.
On Large-Scale Multiple Testing Over Networks: An Asymptotic Approach
We take an asymptotic approach and propose two methods, proportion-matching and greedy aggregation, tailored to distributed settings.
Sample-and-Forward: Communication-Efficient Control of the False Discovery Rate in Networks
This work concerns controlling the false discovery rate (FDR) in networks under communication constraints.
Variable Selection with the Knockoffs: Composite Null Hypotheses
The fixed-X knockoff filter is a flexible framework for variable selection with false discovery rate (FDR) control in linear models with arbitrary design matrices (of full column rank) and it allows for finite-sample selective inference via the Lasso estimates.
Differentially Private Variable Selection via the Knockoff Filter
The knockoff filter, recently developed by Barber and Candes, is an effective procedure to perform variable selection with a controlled false discovery rate (FDR).
