Exponential Family Estimation via Adversarial Dynamics Embedding

NeurIPS 2019 Bo DaiZhen LiuHanjun DaiNiao HeArthur GrettonLe SongDale Schuurmans

We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks. We exploit the primal-dual view of the MLE with a kinetics augmented model to obtain an estimate associated with an adversarial dual sampler... (read more)

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