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.
MCMC for Variationally Sparse Gaussian Processes
Authors: James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,032 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,282 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 | Code to replicate each experiment in this paper is available at github.com/sparse MCMC. |
| Researcher Affiliation | Academia | James Hensman CHICAS, Lancaster University EMAIL Alexander G. de G. Matthews University of Cambridge EMAIL Maurizio Filippone EURECOM EMAIL Zoubin Ghahramani University of Cambridge EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to replicate each experiment in this paper is available at github.com/sparse MCMC. |
| Open Datasets | Yes | We first use the image dataset [29]... coal-mining disaster data... pine sapling data [30]... MNIST The MNIST dataset is a well studied benchmark with a defined training/test split. |
| Dataset Splits | No | For the image dataset, 'The data were split randomly into 1000/1019 train/test sets'; for MNIST, 'The MNIST dataset is a well studied benchmark with a defined training/test split'. While test and training splits are mentioned, an explicit validation split (e.g., percentages or counts) is not provided in the main text. |
| Hardware Specification | No | The paper mentions running experiments 'on a desktop computer' or 'on a desktop machine' but does not provide specific hardware details such as CPU/GPU models, processor types, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Cython implementation' but does not specify version numbers for Cython or any other key software dependencies required for replication. |
| Experiment Setup | Yes | We drew 10,000 samples, discarding the first 1000... ran our sampling scheme using HMC, drawing 3000 samples... ϵ was fixed to 0.001... We used 500 inducing points, initialized from the training data using k-means. |