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..
Robust Unsupervised Learning via L-statistic Minimization
Authors: Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments with KMEANS clustering and principal subspace analysis demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | 1Istituto Italiano di Tecnologia, Genoa, Italy 2University of Genoa, Genoa, Italy 3University of Milan, Milan, Italy 4University College London, London, UK. |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We used the Fashion-MNIST dataset which consists of about 70000 28 28 images of various types of clothes splitted in a training set of 60000 images and a test set of 10000 images. |
| Dataset Splits | No | The paper mentions a training set of 60000 images and a test set of 10000 images, but it does not specify a separate validation set or its size. |
| Hardware Specification | Yes | All experiments have been run on an standard laptop equipped with an Intel i9 with 8 cores each working at 2,4 GHz and 16 GB of RAM DDR4 working at 2,6 GHz. |
| Software Dependencies | No | The paper states, 'For KMEANS++ we used the sklearn implementation', but it does not provide any specific version numbers for sklearn or any other software dependencies. |
| Experiment Setup | Yes | For both RKM and KMEANS++ we T = 10 and r = 30. We initialized RKM with uniform centers and set ζ = 0.75, the same ζ is used for SD. ... We run the algorithms with T = 50, r = 30, M = 4000, k = 2 and ζ in the range [0.4, 1]. |