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.
Evaluating the statistical significance of biclusters
Authors: Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 13,619 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,615 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 | Finally, we present experimental validation of the various tests and biclustering algorithms. |
| Researcher Affiliation | Academia | Jason D. Lee, Yuekai Sun, and Jonathan Taylor Institute of Computational and Mathematical Engineering Stanford University Stanford, CA 94305 |
| Pseudocode | Yes | Algorithm 1 Greedy search algorithm |
| Open Source Code | No | The paper does not contain any statements about releasing source code or links to a code repository. |
| Open Datasets | No | We generate data from the model (1.1) for various values of n and k. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits. It mentions generating data from a model, implying synthetic data without standard splits. |
| Hardware Specification | No | The paper does not provide any details about the specific hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper mentions generating data for various values of n and k and calibrating tests at alpha = 0.1, but it lacks specific hyperparameters or detailed system-level training settings. |