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..
Learning Likelihood-Free Reference Priors
Authors: Nicholas George Bishop, Daniel Jarne Ornia, Joel Dyer, Ani Calinescu, Michael J. Wooldridge
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that good approximations to reference priors for simulation models are in this way attainable, providing a first step towards the development of likelihood-free objective Bayesian inference procedures. ... Here, we present a series of experiments to assess the RP-learning methods described in Section 4. |
| Researcher Affiliation | Academia | 1University of Oxford. Correspondence to: Nicholas Bishop <EMAIL>, Daniel Jarne Ornia <EMAIL>, Joel Dyer <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Flow Pretraining Procedure pretrain Algorithm 2 Training Procedure with Variational Lower Bounds Algorithm 3 Flow Pretraining Procedure pretrain-conditional Algorithm 4 Training for GED |
| Open Source Code | Yes | Code available at https://github.com/joelnmdyer/lf_reference_priors. |
| Open Datasets | Yes | We next consider the popular SBI benchmark task SLCPD (Lueckmann et al., 2021), based on the experiment first introduced by Papamakarios et al. (2019). ... The g-and-k model appears frequently as a benchmark case study for SBI methods (see, e.g., Fearnhead & Prangle, 2012). |
| Dataset Splits | No | The paper describes generating data from simulators (e.g., 'n samples are generated iid from N(ยต, ฯ2)' or 'iid data is generated for t = 1, . . . , n') rather than using predefined splits of a static dataset. Therefore, specific train/test/validation dataset splits are not provided in the conventional sense. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'Py Torch: An Imperative Style, High-Performance Deep Learning Library, 2019.' and 'Adam (Kingma, 2014)'. While PyTorch is a key software component, '2019' is a publication year, not a specific version number. No other software components are mentioned with specific version numbers. |
| Experiment Setup | Yes | Table 4. Hyperparameter settings for Info NCE and SMILE experiments. Table 6. Hyperparameter settings for GED. |