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 [1].
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... |