Michael Gutmann
Senior Lecturer in Machine Learning
Researches machine learning for science, developing methods for Bayesian inference and experimental design in simulator-based models.
Michael Gutmann's lab at Edinburgh works on probabilistic & causal inference and efficient training & inference. 13 papers since 2023, 9 at top venues, mostly TMLR and NeurIPS.
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 69%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 2 · collecting
- 2 papers2025 · T1 1
- 2 papers2024 · T1 1
- 6 papers2023 · T1 5
T1 venuesOtherVenues: TMLR, NeurIPS, AISTATS, ICML, JMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference4 papers
- Efficient training & inference3 papers
- experimental design0 papers
- bayesian inference0 papers
- experimental0 papers
Paper list
From the lab homepage.
20263 papers
- AISTATS (T1)Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
- NeurIPS (T1)Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences (opens in a new tab)
- Cell SystemsRisk-averse optimization of genetic circuits under uncertainty (opens in a new tab)
20252 papers
- PriGMConditional Noise-Contrastive Estimation of Energy-Based Models by Jumping Between Modes
- NeurIPS (T1)Neural Mutual Information Estimation with Vector Copulas
20242 papers
- eLifeDesigning optimal behavioral experiments using machine learning (opens in a new tab)
- TMLR (T1)Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families
20236 papers
- Journal of Statistical SoftwareAn Extendable Python Implementation of Robust Optimisation Monte Carlo
- TMLR (T1)Bayesian Optimization with Informative Covariance
- TMLR (T1)Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
- TMLR (T1)Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
- ICML (T1)Is Learning Summary Statistics Necessary for Likelihood-free Inference?
- JMLR (T1)Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Where alumni went
Alumni are not listed on the lab homepage.
Apply to EdinburghFall 2027
Not announced yet
Informatics PhD
- Round
- Rolling (funding consideration)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Not yet confirmed for this intake.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
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Tiers are venue classifications, not lab rankings.