Chain of Log-Concave Markov Chains
Authors: Saeed Saremi, Ji Won Park, Francis Bach
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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}. |