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..
Gaussian Processes for Survival Analysis
Authors: Tamara Fernandez, Nicolas Rivera, Yee Whye Teh
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report experimental results on synthetic and real data, showing that our model performs better than competing models such as Cox proportional hazards, ANOVA-DDP and random survival forests. |
| Researcher Affiliation | Academia | Tamara Fernández Department of Statistics, University of Oxford. Oxford, UK. EMAIL Nicolas Rivera Department of Informatics, King s College London. London, UK. EMAIL Yee Whye Teh Department of Statistics, University of Oxford. Oxford, UK. EMAIL |
| Pseudocode | Yes | Algorithm 1: Inference Algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We run experiments on the Veteran data, avaiable in the R-package survival package [19]. |
| Dataset Splits | Yes | We perform 10-fold cross validation and compute the C-index for each fold. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using the 'R-package survival package [19]' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | All the experiments are performed using our approximation scheme of equation (6) with a value of m = 50. ... We choose a gamma prior on β and a uniform U(0, 2.3) on α. ... We use elliptical slice sampler [16] for jointly updating the set of coefficients {aj k, bj k} and length-scale parameters. ... We use the maximum likelihood estimator as initial parameters of the model. |