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..
On Divergence Measures for Bayesian Pseudocoresets
Authors: Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate that the pseudocoresets constructed from these methods reflect the true posterior even in high-dimensional Bayesian inference problems. |
| Researcher Affiliation | Collaboration | KAIST1, Stanford University2, NAVER AI Lab3, AITRICS4 EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Bayesian Pseudocoresets with Forward KL |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use the CIFAR10 dataset [15] to generate Bayesian pseudocoresets, and evaluate on the test split of CIFAR10 in addition to the CIFAR10-C dataset [12] |
| Dataset Splits | No | The paper mentions evaluating on the "test split" of CIFAR10 but does not explicitly detail the training/validation splits, their percentages, or refer to a specific citation for how these splits were obtained (e.g., "standard train/validation split from X"). |
| Hardware Specification | Yes | We use 32 cores of Intel Xeon CPU Gold 5120 and 4 Tesla V100s. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies (e.g., Python, PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | As our results were not sensitive to the choice of hyperparameters, we used a single set of hyperparameters that performed best in initial experiments. Please refer to Appendix B for detailed evaluation settings including hyperparameters. |