Gutmann Research Group
Repositories by the Gutmann machine learning group @ The University of Edinburgh.
Pinned Repositories
bedimplicit
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
demiss-vae
[TMLR] Research code for the paper "Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families".
enhanced_discrete_gradient_mcmc
Python code for the paper “Enhanced gradient-based MCMC in discrete spaces”, TMLR 2022, https://openreview.net/forum?id=j2Mid5hFUJ
GradBED
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
idad
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
minebed
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
seqbed
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
tre_code
Python code for the paper "Telescoping Density-Ratio Estimation", NeurIPS 2020
variational-gibbs-inference
Python code for the paper "Variational Gibbs inference for statistical estimation from incomplete data", https://arxiv.org/abs/2111.13180
VNCE
Python code for the paper "Variational Noise-Contrastive Estimation", AISTATS 2019, http://proceedings.mlr.press/v89/rhodes19a/rhodes19a.pdf
Gutmann Research Group's Repositories
gutmanngroup/minebed
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
gutmanngroup/bedimplicit
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
gutmanngroup/demiss-vae
[TMLR] Research code for the paper "Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families".
gutmanngroup/enhanced_discrete_gradient_mcmc
Python code for the paper “Enhanced gradient-based MCMC in discrete spaces”, TMLR 2022, https://openreview.net/forum?id=j2Mid5hFUJ
gutmanngroup/GradBED
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
gutmanngroup/idad
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
gutmanngroup/neural-approx-ss-lfi
Python code "Neural Approximate Sufficient Statistics for Implicit Models", ICLR 2021, https://openreview.net/forum?id=SRDuJssQud
gutmanngroup/seqbed
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
gutmanngroup/tre_code
Python code for the paper "Telescoping Density-Ratio Estimation", NeurIPS 2020
gutmanngroup/variational-gibbs-inference
Python code for the paper "Variational Gibbs inference for statistical estimation from incomplete data", https://arxiv.org/abs/2111.13180
gutmanngroup/VNCE
Python code for the paper "Variational Noise-Contrastive Estimation", AISTATS 2019, http://proceedings.mlr.press/v89/rhodes19a/rhodes19a.pdf