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..
Chain of Log-Concave Markov Chains
Authors: Saeed Saremi, Ji Won Park, Francis Bach
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study our sampling algorithm quantitatively using the 2-Wasserstein metric and compare it with various Langevin MCMC algorithms. We also report a remarkable capacity of our algorithm to tunnel between modes of a distribution. |
| Researcher Affiliation | Collaboration | Saeed Saremi1, Ji Won Park1, Francis Bach2 1Frontier Research, Prescient Design, Genentech, South San Francisco, CA 2Inria, Ecole Normale Supérieure, Université PSL, Paris, France |
| Pseudocode | Yes | Algorithm 1: Sequential multimeasurement walk-jump sampling referred to by SMS. |
| Open Source Code | No | The paper does not provide any explicit statements about code availability or links to source code repositories for the described methodology. |
| Open Datasets | No | The paper uses mathematically defined test densities (e.g., Elliptical Gaussian, Mixture of Gaussians) which do not require external public datasets or specific access information. |
| Dataset Splits | No | The paper conducts experiments on mathematically defined test densities and does not involve traditional dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions specific MCMC algorithms and integration schemes (e.g., 'Sachs et al. (2017)'), but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hyperparameters were tuned on a log-spaced grid. We searched the step size δ over {0.03, 0.1, 0.3, 1.0}, the effective friction γδ over {0.0625, 0.125, 0.25, 0.5, 1.0}, per-t MCMC iterations nt over {1, 4, 16}, and the Lipschitz parameter over {1/σ2, 1.0}. |