Exact Recovery of Mangled Clusters with Same-Cluster Queries
Authors: Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large synthetic datasets confirm that we can reconstruct clusterings exactly and efficiently. |
| Researcher Affiliation | Collaboration | Marco Bressan Dept. of CS, Univ. of Milan, Italy marco.bressan@unimi.it Nicolò Cesa-Bianchi DSRC & Dept. of CS, Univ. of Milan, Italy nicolo.cesa-bianchi@unimi.it Silvio Lattanzi Google silviol@google.com Andrea Paudice Dept. of CS, Univ. of Milan, Italy & Istituto Italiano di Tecnologia, Italy andrea.paudice@unimi.it |
| Pseudocode | Yes | Algorithm 1 Tessellation Learn(X, SC, γ) and Algorithm 2 RECUR(X, k, γ, ε) |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper states, "We generated four synthetic instances on n = 105 points with increasing dimension d = 2, 4, 6, 8." This describes the generation of synthetic data but does not provide access to a public dataset with a link, citation, or repository information. |
| Dataset Splits | No | The paper does not explicitly describe training, validation, or test dataset splits. It mentions using synthetic datasets for experiments but no partitioning details. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper does not provide any specific software names with version numbers or list any software dependencies. |
| Experiment Setup | Yes | The latent clusterings consist of k = 5 ellipsoidal clusters of equal size, each one with margin γ = 1 w.r.t. a random center and a random PSD matrix with condition number κ = 100, making each cluster stretched by 10 in a random direction. To account for an imperfect knowledge of the data, we fed RECUR with a value of γ = 10 (...) We also adopted for RECUR the batch sampling of SCQ-k-means, i.e., we draw k 10 samples in each round; |