Michael U. Gutmann

Publications47
h-index15
Citations991
Highly Influential Citations122
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VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
TLDR
We introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Expand
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Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
TLDR
We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference of simulator-based statistical models. Expand
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Fundamentals and Recent Developments in Approximate Bayesian Computation
&NA; Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and… Expand
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Likelihood-free inference by ratio estimation
TLDR
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Expand
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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
TLDR
We propose a new method for detecting changes in Markov network structure between two sets of samples. Expand
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Statistical Inference of Intractable Generative Models via Classification
Increasingly complex generative models are being used across the disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large… Expand
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ELFI: Engine for Likelihood Free Inference
Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as… Expand
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Conditional Noise-Contrastive Estimation of Unnormalised Models
TLDR
We propose a new method for estimating unnormalised statistical models that leverages the observed data when generating noise samples. Expand
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Efficient acquisition rules for model-based approximate Bayesian computation
TLDR
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. Expand
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Variational Noise-Contrastive Estimation
TLDR
We propose variational noise-contrastive estimation (VNCE), a method that can be used for both parameter estimation of unnormalised models and posterior inference of latent variables. Expand
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