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..
Clustering Semi-Random Mixtures of Gaussians
Authors: Aravindan Vijayaraghavan, Pranjal Awasthi
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a natural robust model for k-means clustering that generalizes the Gaussian mixture model, and that we believe will be useful in identifying robust algorithms. Our first contribution is a polynomial time algorithm that provably recovers the ground-truth up to small classification error w.h.p., assuming certain separation between the components. Perhaps surprisingly, the algorithm we analyze is the popular Lloyd s algorithm for k-means clustering that is the method-of-choice in practice. Our second result complements the upper bound by giving a nearly matching lower bound on the number of misclassified points incurred by any k-means clustering algorithm on the semi-random model. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Rutgers University, USA. 2EECS Department, Northwestern University, USA. |
| Pseudocode | Yes | Algorithm 1 Lloyd s Algorithm Input: A be the N d data matrix with rows Ai for i [N]. Use A to compute initial centers µ(1) 0 , µ(2) 0 , . . . µ(k) 0 as detailed in Proposition 3.2. Use these k-centers to seed a series of Lloyd-type iterations i.e., for r = 1, 2, . . .: do Set Zi be the set of points for which the closest center among µ(1) r 1, µ(2) r 1, . . . , µ(k) r 1 is µ(i) r 1. Set µ(i) r 1 |Zi| P Aj Zi Aj. end for |
| Open Source Code | No | The paper does not contain any statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and focuses on a mathematical model (semi-random GMM). It does not use or provide access information for a specific public dataset for experimental training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with training, validation, and test dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and algorithms; it does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical analysis and algorithms; it does not describe any empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |