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 What Matters: Optimal Approximation for Clustering with Outliers
Authors: Akanksha Agrawal, Tanmay Inamdar, Saket Saurabh, Jie Xue
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we give a general approach for solving clustering with outliers, which results in a fixed-parameter tractable (FPT) algorithm in k and m (i.e., an algorithm with running time of the form f(k, m) n O(1) for some function f), that almost matches the approximation ratio for its outlier-free counterpart. As a corollary, we obtain FPT approximation algorithms with optimal approximation ratios for k-MEDIAN and k-MEANS with outliers in general metrics. |
| Researcher Affiliation | Academia | 1 Indian Institute of Technology Madras, Chennai, India. 2 University of Bergen, Bergen, Norway 3 The Institute of Mathematical Sciences, HBNI, Chennai, India 4 New York University Shanghai, China |
| Pseudocode | No | The algorithm steps are described procedurally in paragraph form (e.g., 'We enumerate every subset T S of size at most m. For each T, we compute a β-approximation solution...'), but there is no formally structured pseudocode block, algorithm box, or figure labeled as such. |
| Open Source Code | No | A full version of the paper containing missing proofs and other details can be found is available on ar Xiv:2212.00696 (Agrawal et al. 2022). This refers to a paper version, not code. There is no explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | No | This is a theoretical paper focused on algorithm design and approximation proofs, and as such, it does not use or refer to any specific dataset for training, validation, or testing. |
| Dataset Splits | No | This is a theoretical paper focused on algorithm design and approximation proofs, and as such, it does not specify any dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper focused on algorithm design and approximation proofs, and as such, it does not mention any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper focused on algorithm design and approximation proofs, and as such, it does not mention any specific software dependencies or versions. |
| Experiment Setup | No | This is a theoretical paper focused on algorithm design and approximation proofs, and as such, it does not describe any experimental setup details such as hyperparameters or training configurations. |