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.
A Non–Convex Optimization Approach to Correlation Clustering
Authors: Erik Thiel, Morteza Haghir Chehreghani, Devdatt Dubhashi5159-5166
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,480 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,049 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 | We demonstrate the performance on both synthetic and real world data sets. |
| Researcher Affiliation | Academia | Chalmers University of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Block-coordinate Frank-Wolfe Algorithm on a Product Domain |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We demonstrate the performance on both synthetic and real world data sets. We formalize this idea using the World Color Survey dataset consisting of human labellings of 330 color tiles in different languages. In this section, we study the performance of different algorithms on several subsets of 20 newsgroup data collection. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The parameters are: n = 300, k = 10, p = 0.3, and the batch size b = 10 (i.e., q = 0.03). We fix the number of cluster k = 5. Then for a selected n, we assign each object randomly to one of the five different clusters. The weight between two nodes (objects) is a standard normal random variable. |