Deep Direct Likelihood Knockoffs

NIPS 2020, 2020.

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Deep Direct Likelihood Knockoffs is a generative model for sampling knockoffs that directly minimizes a Kullback–Leibler divergence divergence implied by the knockoff swap property

Abstract:

Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often wishes to discover which features are actually important for making the predictions. These discoveries may lead to costly follow-up experiments and as such i...More

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