Gutmann Research Group

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11 repositories

demiss-vae
Public
[TMLR] Research code for the paper "Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families".
Python
•1•0•0•0•Updated Jun 26, 2024Jun 26, 2024
variational-gibbs-inference
Public
Python code for the paper "Variational Gibbs inference for statistical estimation from incomplete data", https://arxiv.org/abs/2111.13180
Python
•2•0•0•0•Updated Sep 7, 2023Sep 7, 2023
enhanced_discrete_gradient_mcmc
Public
Python code for the paper “Enhanced gradient-based MCMC in discrete spaces”, TMLR 2022, https://openreview.net/forum?id=j2Mid5hFUJ
sampling mcmc discrete-variables gradient-based
Python
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MIT License
•1•0•0•0•Updated Nov 29, 2022Nov 29, 2022
neural-approx-ss-lfi
Public
Python code "Neural Approximate Sufficient Statistics for Implicit Models", ICLR 2021, https://openreview.net/forum?id=SRDuJssQud
Jupyter Notebook
•8•0•0•0•Updated Jun 23, 2022Jun 23, 2022
idad
Public
Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html
bayesian-experimental-design adaptive-design likelihood-free-inference implicit-models
Python
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MIT License
•6•0•0•0•Updated Dec 30, 2021Dec 30, 2021
tre_code
Public
Python code for the paper "Telescoping Density-Ratio Estimation", NeurIPS 2020
ratio-estimation mutual-information-estimation energy-based-deep-learning
Python
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MIT License
•4•0•0•0•Updated Sep 24, 2021Sep 24, 2021
GradBED
Public
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
bayesian-experimental-design likelihood-free-inference implicit-models
Python
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MIT License
•2•0•0•0•Updated May 10, 2021May 10, 2021
minebed
Public
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
bayesian-experimental-design likelihood-free-inference implicit-models
Python
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MIT License
•4•1•0•0•Updated Apr 27, 2021Apr 27, 2021
VNCE
Public
Python code for the paper "Variational Noise-Contrastive Estimation", AISTATS 2019, http://proceedings.mlr.press/v89/rhodes19a/rhodes19a.pdf
variational-inference latent-variable-models noise-contrastive-estimation
Jupyter Notebook
•1•0•0•0•Updated Aug 31, 2020Aug 31, 2020
bedimplicit
Public
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
bayesian-experimental-design experimental-design likelihood-free-inference implicit-models
Python
•2•0•0•0•Updated Aug 26, 2020Aug 26, 2020
seqbed
Public
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
bayesian-experimental-design likelihood-free implicit-models
Python
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MIT License
•4•0•0•0•Updated May 29, 2020May 29, 2020