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 [1].
Is Learning Summary Statistics Necessary for Likelihood-free Inference?
Authors: Yanzhi Chen, Michael U. Gutmann, Adrian Weller
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on diverse inference tasks with different data types validate our hypothesis. 5. Experiments |
| Researcher Affiliation | Academia | 1Department of Engineering, Cambridge University, UK 2School of Informatics, The University of Edinburgh, UK. |
| Pseudocode | Yes | Algorithm 1 Slice sufficient statistics learning; Algorithm 2 SNL with slice sufficient statistics |
| Open Source Code | No | No explicit statement of releasing open-source code for the methodology described in this paper or a direct link to a code repository was found. |
| Open Datasets | No | No concrete access information (link, DOI, formal citation with authors/year) for a publicly available or open dataset was provided. The paper states: "It only requires us to sample (i.e. simulate) data from the model." and "Input: simulated data D = {θ(j), x(j)}n j=1". |
| Dataset Splits | Yes | For all our experiments, we use early stopping to train all neural networks, where we use 80% of the data in training and 20% in validation (the patience threshold is 500 iterations). |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory amounts, or cloud instance names) used for running experiments were mentioned. The paper only discusses 'execution time' without specifying the hardware on which it was measured. |
| Software Dependencies | No | No specific software dependency versions were provided. The paper mentions using 'masked autoregressive flow (MAF)' for density estimation and 'Adam' optimizer, but without version numbers for these or other libraries. |
| Experiment Setup | Yes | Throughout the experiments we use M = 8 slices and set d = K (except for the experiments where we select d according to Section 3.2) and d = 2. For all our experiments, we use early stopping to train all neural networks... The learning rate is 5 10 4. A batch size of 200 is used for all networks. |