Differentiable Subset Sampling
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Many machine learning tasks require sampling a subset of items from a collection. Due to the non-differentiability of subset sampling, the procedure is usually not included in end-to-end deep learning models. We show that through a connection to weighted reservoir sampling, the Gumbel-max trick can be extended to produce exact subset samples, and that a recently proposed top-k relaxation can be used to differentiate through the subset sampling procedure. We test our method on end-to-end tasks requiring subset sampling, including a differentiable k-nearest neighbors task and an instance-wise feature selection task for model interpretability.
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