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Sampling is a fundamental technique, and sampling without replacement isoften desirable when duplicate samples are not beneficial. Within machinelearning, sampling is useful for generating diverse outputs from a trainedmodel. We present an elegant procedure for sampling without replacement from abroad class of randomized programs, including generative neural models thatconstruct outputs sequentially. Our procedure is efficient even forexponentially-large output spaces. Unlike prior work, our approach isincremental, i.e., samples can be drawn one at a time, allowing for increasedflexibility. We also present a new estimator for computing expectations fromsamples drawn without replacement. We show that incremental sampling withoutreplacement is applicable to many domains, e.g., program synthesis andcombinatorial optimization.

Incremental Sampling Without Replacement for Sequence Models