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 Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
Authors: Weijia Zhang, Chun Kai Ling, Xuanhui Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments results from a wide range of datasets demonstrate that our approach successfully discerns the underlying dependency structure and significantly reduces survival estimation bias when compared to existing methods. |
| Researcher Affiliation | Academia | 1 School of Information and Physical Sciences, The University of Newcastle, Australia 2 Carnegie Mellon University, USA 3 School of Information Management, Nanjing University, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/Weijia Zhang24/DCSurvival |
| Open Datasets | Yes | We use two semi-synthetic datasets based on the STEEL (V E, Shin, and Cho 2020) which contains 35,040 samples with 9 covariates, and the Airfoil (Thomas Brooks 1989) datasets which includes 1503 samples with 6 covariates. ... The SEER dataset is from the Surveillance, Epidemiology and End Results database (Howlader et al. 2010). ... GBSG2 is from the German Breast Cancer Study Group (Schumacher et al. 1994) |
| Dataset Splits | Yes | We use Adam W (Loshchilov and Hutter 2019) for optimization and use 50%/30%/20% training/validation/test splits. |
| Hardware Specification | Yes | Experiments are conducted with one NVIDIA RTX4090 GPU. |
| Software Dependencies | No | We utilize Pytorch (Paszke et al. 2017) for implementing all neural networks and automatic differentiation. |
| Experiment Setup | Yes | Tensors are computed with double precision (fp64) as the inversion of φ mandates numerical precision. When using Newton s method to compute the inverse φ 1 nn, we terminate when the error is less than 1 10 12. For all our experiments we set φnn with L = 2 and H1 = H2 = 10, i.e., the copula representation contains two hidden layers with each of width 10. ... We use Adam W (Loshchilov and Hutter 2019) for optimization and use 50%/30%/20% training/validation/test splits. We used validation samples for early stopping based on the validation log-likelihood. |