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..
Inverse-Weighted Survival Games
Authors: Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler Perotte, Rajesh Ranganath
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that these games optimize BS on simulations and then apply these principles on real world cancer and critically-ill patient data. |
| Researcher Affiliation | Academia | Xintian Han NYU EMAIL Mark Goldstein NYU EMAIL Aahlad Puli NYU EMAIL Thomas Wies NYU EMAIL Adler J. Perotte Columbia University EMAIL Rajesh Ranganath NYU EMAIL |
| Pseudocode | Yes | Algorithm 1 Following Gradients in Summed Games |
| Open Source Code | Yes | Code is available at https://github.com/rajesh-lab/Inverse-Weighted-Survival-Games |
| Open Datasets | Yes | Data. Survival-MNIST [Gensheimer, 2019, Pölsterl, 2019] draws times conditionally on MNIST label Y. ... We use several datasets used in recent papers [Chen, 2020, Kvamme et al., 2019] and available in the python packages Deep Surv [Katzman et al., 2018] and Py Cox [Kvamme et al., 2019], and the R Survival [Therneau, 2021]. The datasets are: Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) [Curtis et al., 2012] Rotterdam Tumor Bank (ROTT) [Foekens et al., 2000] and German Breast Cancer Study Group (GBSG) [Schumacher et al., 1994] combined into one dataset (ROTT. & GBSG) Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT) [Knaus et al., 1995] |
| Dataset Splits | Yes | For all datasets, we created a random 80/10/10 train/validation/test split for training and evaluation. |
| Hardware Specification | Yes | All models were trained on a single NVIDIA Quadro RTX 8000 GPU |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Training was performed using Adam optimizer [Kingma and Ba, 2015] with a learning rate of 0.001. ... We used a batch size of 256 for all datasets except METABRIC for which we used a batch size of 128. ... All models were trained for 500 epochs with early stopping based on the validation set negative log likelihood... |