Correlation Clustering with Adaptive Similarity Queries
Authors: Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our algorithms empirically on real-world and synthetic datasets. |
| Researcher Affiliation | Academia | Marco Bressan Department of Computer Science University of Rome Sapienza Nicolò Cesa-Bianchi Department of Computer Science & DSRC Università degli Studi di Milano Andrea Paudice Department of Computer Science Università degli Studi di Milano & IIT Fabio Vitale Department of Computer Science University of Lille & Inria |
| Pseudocode | Yes | Algorithm 1 ACC with query rate f |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We verify experimentally the tradeoff between clustering cost and number of queries of ACC, using six datasets from [21, 20]. Four datasets come from real-world data, and two are synthetic; all of them provide a ground-truth partitioning of some set V of nodes. |
| Dataset Splits | No | The paper mentions running independent executions and computing average costs, but it does not specify how the data was split into training, validation, or test sets, nor does it refer to specific cross-validation schemes. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | For α = 0, 0.05, ..., 0.95, 1, we set the query rate function to f(x) = xα. Then we ran 20 independent executions of ACC, and computed the average number of queries µQ and average clustering cost µ . |