On fenchel mini-max learning
Inference, estimation, sampling and likelihood evaluation are four primary goals of probabilistic modeling. Practical considerations often force modeling approaches to make compromises between these objectives. We present a novel probabilistic learning framework, called Fenchel Mini-Max Learning (FML), that accommodates all four desiderata in a flexible and scalable manner. Our derivation is rooted in classical maximum likelihood estimation, and it overcomes a longstanding challenge that prevents unbiased estimation of unnormalized statistical models. By reformulating MLE as a mini-max game, FML enjoys an unbiased training objective that (i) does not explicitly involve the intractable normalizing constant and (ii) is directly amendable to stochastic gradient descent optimization. To demonstrate the utility of the proposed approach, we consider learning unnormalized statistical models, nonparametric density estimation and training generative models, with encouraging empirical results presented.
Duke Scholars
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Related Subject Headings
- 4611 Machine learning
- 1702 Cognitive Sciences
- 1701 Psychology
Citation
Published In
ISSN
Publication Date
Volume
Related Subject Headings
- 4611 Machine learning
- 1702 Cognitive Sciences
- 1701 Psychology