Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
Authors: Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Koethe, Paul-Christian Bürkner, Stefan T. Radev
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Empirical Evaluation We evaluate our self-consistent estimator across a range of synthetic tasks and real-world problems. |
| Researcher Affiliation | Academia | 1University of Stuttgart, Germany 2University of Oxford, UK 3TU Dortmund University, Germany 4Heidelberg University, Germany 5Rensselaer Polytechnic Institute, USA. |
| Pseudocode | Yes | Algorithm 1 Self-consistency loss for finite training. {I}: likelihood-based with analytic likelihood {II}: simulation-based with approximate likelihood |
| Open Source Code | Yes | Code Availability We provide reproducible code in the open repository at https://github.com/marvinschmitt/ self-consistency-abi |
| Open Datasets | Yes | As a scientific real-world example, we apply our method to an experimental data set in biology (Silk et al., 2011). |
| Dataset Splits | No | No specific details on dataset split percentages, counts, or methodology for training/validation/test sets were provided. While 'posterior loss on a separate validation dataset' is mentioned in Appendix E, the exact details of this split (e.g., size, methodology) are not specified. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for experiments, such as GPU/CPU models, memory, or cloud instance specifications. |
| Software Dependencies | No | The paper mentions software like 'Stan', 'neural spline flow', 'Deep Set', 'tensorflow_probability', and 'scipy.stats', but does not provide specific version numbers for these software dependencies, only citations to their original papers. |
| Experiment Setup | Yes | The neural networks are trained for a total of 35 epochs with a batch size of 32 and an initial learning rate of 10-3. We choose a stepwise constant annealing schedule for the self-consistency weight λ such that λ = 0 for the first 5 epochs, and λ = 1 for the remaining 30 epochs. |