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..
Multitask Boosting for Survival Analysis with Competing Risks
Authors: Alexis Bellot, Mihaela van der Schaar
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our algorithm on synthetic and real data. ... 5 Experiments ... 5.2 Synthetic Studies ... 5.3 Real data studies: SEER |
| Researcher Affiliation | Academia | Alexis Bellot University of Oxford Oxford, United Kingdom EMAIL Mihaela van der Schaar University of Oxford and The Alan Turing Institute London, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 Multitask Boosting |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of code release) for the source code. |
| Open Datasets | Yes | We investigate a patient population extracted from the Surveillance, Epidemiology, and End Results (SEER) repository similarly to [2]. ... We generate 5 data sets (by sampling variables and parameters randomly) of 1000 instances for each individual scenario and set a random 50% of the population to be uniformly censored. |
| Dataset Splits | Yes | Deep Learning architecture (Deep Hit) [18] with hyperparameters optimized with a validation set. ... these are averages over 4 fold cross-validation estimates and confidence bands are standard deviations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of statistical methods and models but does not specify any software dependencies with version numbers (e.g., specific Python libraries or statistical packages with their versions). |
| Experiment Setup | Yes | On all experiments we train SMTBoost with a tree-depth of 3 and 250 boosting iterations, our default parameter settings. |