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Speaker: Lang Liu

Title: The Sample Complexity of Statistical Comparison Between Generative Models

Abstract: The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models. Although practically successful, the sample complexity of the empirical estimator of divergence frontiers is unknown. We establish non-asymptotic bounds on the sample complexity of divergence frontiers, providing theoretical guidance on their estimation procedure.

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