Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Censor Dependent Variational Inference
Authors: Chuanhui Liu, Xiao Wang
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 32,509 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,102 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate our analysis and demonstrate significant improvements in the estimation of individual survival distributions. Codes can be found at https://github.com/Chuanhui Liu/CDVI. (...) Section 5 presents empirical validation and findings through extensive experiments on various datasets. Notably, our methods achieve 5% higher C-index than state-of-the-art models on WHAS datasets. |
| Researcher Affiliation | Academia | Chuanhui Liu 1 Xiao Wang 1 (...) 1Department of Statistics, Purdue University, USA. Correspondence to: Xiao Wang <EMAIL>. |
| Pseudocode | No | The paper describes methods and proofs but does not contain a clearly labeled section or figure titled "Pseudocode" or "Algorithm", nor does it present structured, code-like procedural steps. |
| Open Source Code | Yes | Codes can be found at https://github.com/Chuanhui Liu/CDVI. |
| Open Datasets | Yes | Table 3: Summary table for benchmark clinical datasets. (...) Table 3 summarizes the real-world datasets. These models include Cox-PH (Cox, 1972)... |
| Dataset Splits | Yes | Train-validation-test split ratio is 0.6, 0.2, 0.2. Experiment repetition is 5, using the same seeds of dataset split. |
| Hardware Specification | No | The experiments are on Python 3.9 with Pytorch on the Windows 11 system. GPU is not required. No specific GPU or CPU models, memory details, or detailed computer specifications are provided. |
| Software Dependencies | No | The experiments are on Python 3.9 with Pytorch on the Windows 11 system. (...) All models were implemented using the Python package by Nagpal et al. (2022), and our implementation follows its API for ease of reproducibility. (...) C index is implemented via Python package Pycox by the authors of Kvamme et al. (2019) (...) Ctd and Brier score is implemented via Python package Scikit-Survival (P olsterl, 2020). While Python 3.9 is specified, other key software components like Pytorch, Pycox, or Scikit-Survival do not have explicit version numbers mentioned. |
| Experiment Setup | Yes | C.3. Hyper-parameters of training CD-CVAE and the variants (...) We have tuned the following hyper-parameters: Distribution Family of decoders: (...) Dropout: (...) Latent dimension: (...) Learning rate: 0.01, 0.001. batch size: 20, 100, 250, 500, 1000. Patience: (...) Temperature: (...) |