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 Testing of Samplers
Authors: Kuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a prototype implementation of Barbarik2 and use it to test three state-of-the-art samplers. To demonstrate the practical efficiency of Barbarik2, we developed a prototype implementation in Python and performed an experimental evaluation with several samplers. |
| Researcher Affiliation | Academia | 1School of Computing, National University of Singapore 2Indian Statistical Institute, Kolkata |
| Pseudocode | Yes | Algorithm 1 Barbarik2(G, A, ε, η, δ, ϕ, S, wt) Algorithm 2 Barbarik2Kernel(ϕ, σ1, σ2) Algorithm 3 Bias(ˆσ, Γ, S) |
| Open Source Code | Yes | The accompanying tool, available open source, can be found at https://github.com/meelgroup/barbarik. |
| Open Datasets | Yes | We conducted our experiments on 72 publicly available benchmarks, which have been employed in the evaluation of samplers proposed in the past [13, 21]. |
| Dataset Splits | No | The paper uses publicly available benchmarks to evaluate samplers but does not describe any specific training, validation, or test dataset splits for these benchmarks. |
| Hardware Specification | Yes | All experiments were conducted on a high performance computing cluster with 600 E5-2690 v3 @2.60GHz CPU cores. |
| Software Dependencies | No | The paper mentions 'Python' for implementation and uses 'WAPS' as an ideal sampler, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper specifies test parameters like tolerance, intolerance, and confidence values, and mentions instantiating Barbarik2Kernel with m=12 and k=2m-1. However, it does not provide explicit hyperparameters related to model training or system-level training settings as typically found in machine learning experimental setups. |