Incremental Sampling Without Replacement for Sequence Models
This provides a flexible sampling method for machine learning practitioners needing diverse outputs without duplicates, though it appears incremental in nature.
The paper tackles the problem of sampling without replacement from generative models, presenting an incremental procedure that allows drawing samples one at a time, which is efficient for large output spaces and applicable to domains like program synthesis and combinatorial optimization.
Sampling is a fundamental technique, and sampling without replacement is often desirable when duplicate samples are not beneficial. Within machine learning, sampling is useful for generating diverse outputs from a trained model. We present an elegant procedure for sampling without replacement from a broad class of randomized programs, including generative neural models that construct outputs sequentially. Our procedure is efficient even for exponentially-large output spaces. Unlike prior work, our approach is incremental, i.e., samples can be drawn one at a time, allowing for increased flexibility. We also present a new estimator for computing expectations from samples drawn without replacement. We show that incremental sampling without replacement is applicable to many domains, e.g., program synthesis and combinatorial optimization.