Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Data-Driven Clustering via Parameterized Lloyd's Families
Authors: Maria-Florina F. Balcan, Travis Dick, Colin White
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we show positive theoretical and empirical results for learning the best initialization and local search procedures over a large family of algorithms. and In this section, we empirically evaluate the effect of the α parameter on clustering cost for realworld and synthetic clustering domains. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Department of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 EMAIL Travis Dick Department of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 EMAIL Colin White Department of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1 (α, β)-Lloyds++ Clustering |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We ran experiments on datasets including MNIST, CIFAR10, CNAE9, and a synthetic Gaussian Grid dataset. |
| Dataset Splits | Yes | We generate m = 50, 000 samples from each distribution and divide them into equal-sized training and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cluster specifications) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | For MNIST and CIFAR10 we set k = 5, and N = 100, while for CNAE9 and the Gaussian Grid we set k = 4 and N = 120. and We always measure distance between points using the ℓ2 distance and set β = 2. |