495 citations · 932 across the 37 of their papers we have counts for
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Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)
Traiko Dinev, Michael U. Gutmann
Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable ac…
Efficient Bayesian Experimental Design for Implicit Models
Steven Kleinegesse, Michael Gutmann
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likeliho…
Variational Noise-Contrastive Estimation
Benjamin Rhodes, Michael Gutmann
Unnormalised latent variable models are a broad and flexible class of statistical models. However, learning their parameters from data is intractable, and few estimation techniques…
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan, Michael U. Gutmann
Many parametric statistical models are not properly normalised and only specified up to an intractable partition function, which renders parameter estimation difficult. Examples of…
Generative Ratio Matching Networks
Akash Srivastava, Kai Xu, Michael U. Gutmann +1
Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires…