Computer Science > Machine Learning
[Submitted on 27 Apr 2019 (v1), last revised 30 Mar 2020 (this version, v3)]
Title:Exponential Family Estimation via Adversarial Dynamics Embedding
View PDFAbstract: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. To represent this sampler, we introduce a novel neural architecture, dynamics embedding, that generalizes Hamiltonian Monte-Carlo (HMC). The proposed approach inherits the flexibility of HMC while enabling tractable entropy estimation for the augmented model. By learning both a dual sampler and the primal model simultaneously, and sharing parameters between them, we obviate the requirement to design a separate sampling procedure once the model has been trained, leading to more effective learning. We show that many existing estimators, such as contrastive divergence, pseudo/composite-likelihood, score matching, minimum Stein discrepancy estimator, non-local contrastive objectives, noise-contrastive estimation, and minimum probability flow, are special cases of the proposed approach, each expressed by a different (fixed) dual sampler. An empirical investigation shows that adapting the sampler during MLE can significantly improve on state-of-the-art estimators.
Submission history
From: Bo Dai [view email][v1] Sat, 27 Apr 2019 01:20:21 UTC (2,215 KB)
[v2] Sun, 1 Dec 2019 06:36:27 UTC (2,079 KB)
[v3] Mon, 30 Mar 2020 20:20:43 UTC (2,077 KB)
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