Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Automatic Posterior Transformation for Likelihood-Free Inference
Authors: David Greenberg, Marcel Nonnenmacher, Jakob Macke
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare APT to SNPE-A, SNPE-B and SNL on several problems (implementation details in A.5). |
| Researcher Affiliation | Academia | 1Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany. |
| Pseudocode | Yes | Algorithm 1 APT with per-round proposal updates |
| Open Source Code | Yes | Code available at github.com/mackelab/delο¬. |
| Open Datasets | No | The paper uses various simulators (e.g., two-moons, SLCP, Lotka-Volterra) to generate data for experiments, but does not provide specific access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating simulations in rounds but does not specify exact training, validation, or test split percentages, sample counts, or citations to predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions 'implemented in PyTorch' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | All models were implemented in PyTorch (Paszke et al., 2017) and trained using Adam (Kingma & Ba, 2014) with an initial learning rate of 10 3 which was decayed by a factor of 10 if the validation loss did not decrease for 20 epochs. |