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.
Class Prior Estimation with Biased Positives and Unlabeled Examples
Authors: Shantanu Jain, Justin Delano, Himanshu Sharma, Predrag Radivojac4255-4263
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,576 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,771 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 | Our empirical investigation suggests feasibility of the correction strategy and overall good performance. Experiments and Results summarize our empirical investigation, summarizing the datasets, experimental protocols and results. |
| Researcher Affiliation | Academia | Shantanu Jain, Justin D. Delano, Himanshu Sharma, Predrag Radivojac Khoury College of Computer Sciences Northeastern University, Boston, MA, U.S.A. |
| Pseudocode | Yes | Algorithm 1 Algorithm for class prior estimation with biased positives and unlabeled examples. // max K specifies the maximum number of clusters. Require: M, C, max K Ensure: α // Partition the biased positive set by k-means clustering. // The number of clusters is picked to be the one giving // a clustering with the maximum Silhouette coefficient, // up to a maximum of max K. c Part[i] stores the // positives in the ith cluster. c Part k Means Silhouette(C, max K) |
| Open Source Code | No | The paper does not provide explicit statements or links for the open-sourcing of the described methodology's code. |
| Open Datasets | Yes | Our experiments were carried out on twelve real-life datasets from the UCI Machine Learning Repository (Lichman 2013). |
| Dataset Splits | No | The paper describes the generation of biased and unbiased positive-unlabeled datasets but does not explicitly provide training, validation, and test splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms and libraries (e.g., 'k-means algorithm', 'Alpha Max', 'Elkan-Noto algorithm') but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Corrected is an exact implementation of Algorithm 1 with max K intialized to 5. To generate biased positive examples and unlabeled data, the positive examples were clustered using k-means, where the number of clusters, K, was determined based on the Silhouette coefficient. |