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..
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Authors:
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models. |
| Researcher Affiliation | Academia | Ahmed M. Alaa Electrical Engineering Department University of California, Los Angeles EMAIL Mihaela van der Schaar Department of Engineering Science University of Oxford EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | We conducted experiments on a real-world patient cohort extracted from a publicly accessible dataset provided by the Surveillance, Epidemiology, and End Results Program (SEER). https://seer.cancer.gov/causespecific/ |
| Dataset Splits | Yes | We divide D into 500 subjects for training and 500 subjects for out-of-sample testing. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'R libraries cmprsk and survival', 'R-package threg', and 'R-package pec', and the 'ADAM algorithm', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We run the algorithm in Section 4 with Q = 3 outputs for the ο¬rst layer of the DMGP, and we use the default settings prescribed in [18] for the ADAM algorithm. |