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..
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Authors: Louis Sharrock, Christopher Nemeth
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the performance of our approach on a range of numerical examples, including several high-dimensional models and datasets, demonstrating comparable performance to other Par VI algorithms with no need to tune a learning rate. |
| Researcher Affiliation | Academia | 1Department of Mathematics, Lancaster University, UK. |
| Pseudocode | Yes | Algorithm 1 Coin Wasserstein Gradient Descent |
| Open Source Code | Yes | Code to reproduce our numerical results can be found at https://github.com/louissharrock/Coin-SVGD. |
| Open Datasets | Yes | We test our algorithm using the Covertype dataset, which consists of 581,012 data points and 54 features. (Gershman et al., 2012) [...] We test the performance of our algorithms on several UCI datasets. (Liu & Wang, 2016; Hernandez-Lobato & Adams, 2015) [...] We test our algorithm on the Movie Lens dataset (Harper & Konstan, 2015)... |
| Dataset Splits | Yes | We randomly partition the data into a training dataset (70%), validation dataset (10%), and testing dataset (20%). |
| Hardware Specification | Yes | We perform all experiments using a Mac Book Pro 16 (2021) laptop with Apple M1 Pro chip and 16GB of RAM. |
| Software Dependencies | No | The paper mentions 'Python 3, Py Torch, Theano, and Jax' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In all cases, we run both algorithms using N = 20 particles, and for T = 1000 iterations. We initialise the particles according to (Îļi 0)N i=1 i.i.d. N(0, 0.12). |