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..
A Langevin-like Sampler for Discrete Distributions
Authors: Ruqi Zhang, Xingchao Liu, Qiang Liu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive experimental results, including Ising models, restricted Boltzmann machines, deep energy-based models, binary Bayesian neural networks and text generation, to demonstrate the superiority of DLP in general settings. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin. Correspondence to: Ruqi Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Samplers with Discrete Langevin Proposal (DULA and DMALA). |
| Open Source Code | Yes | We released the code at https://github. com/ruqizhang/discrete-langevin. |
| Open Datasets | Yes | Following Grathwohl et al. (2021), we train {W, a, b} with contrastive divergence (Hinton, 2002) on the MNIST dataset for one epoch. and We conduct regression on four UCI datasets (Dua & Graff, 2017). |
| Dataset Splits | Yes | The reported results are from the model which performs the best on the validation set. |
| Hardware Specification | No | The paper mentions that computations can be done "in parallel on CPUs and GPUs" but does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper states "All methods are implemented in Pytorch" but does not provide specific version numbers for PyTorch or any other software dependencies needed for replication. |
| Experiment Setup | Yes | DMALA and DULA use a stepsize α of 0.4 and 0.2 respectively. and We set the batch size to 100. The stepsize α is set to be 0.1 for DULA and 0.2 for DMALA, respectively. The model is optimized by an Adam optimizer with a learning rate of 0.001. |