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A Unified f-divergence Framework Generalizing VAE and GAN
Developing deep generative models that flexibly incorporate diverse measures of probability distance is an important area of research.
Discovering Conditionally Salient Features with Statistical Guarantees
Most of the work in this domain has focused on identifying globally relevant features, which are features that are related to the outcome using evidence across the entire dataset.
Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization
The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees.
Knockoffs for the mass: new feature importance statistics with false discovery guarantees
This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features.
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