Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Chain of Log-Concave Markov Chains
Authors: Saeed Saremi, Ji Won Park, Francis Bach
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 26,379 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,750 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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}. |