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..
An Efficient Pruning Algorithm for Robust Isotonic Regression
Authors: Cong Han Lim
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that this algorithm can perform much faster than the dynamic programming approach on robust estimators, especially as the desired accuracy increases. |
| Researcher Affiliation | Academia | Cong Han Lim School of Industrial Systems and Engineering Georgia Tech Altanta, GA 30332 EMAIL Work done while at Wisconsin Institute for Discovery, University of Wisconsin-Madison. |
| Pseudocode | Yes | Algorithm 1 Dynamic Program for fixed grid Gk; Algorithm 2 Algorithmic Framework for Faster Robust Isotonic Regression; Algorithm 3 Pruning I via Lower/Upper Bounds; Algorithm 4 Pruning Subroutine; Algorithm 5 Main Algorithm for Refining via Linear Underestimators; Algorithm 6 A Pruning Algorithm for Robust Isotonic Regression |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or provide any links to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data: 'We generate a series of n points y1, . . . , yn from 0.2 to 0.8 equally spaced out and added Gaussian random noise with standard deviation of 0.03. We then randomly flipped between 5% to 50% of the points around 0.5, and these points act as the outliers.' It does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes how the data was generated and used in experiments but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | The algorithms were implemented in Python 3.6.7, and the experiments were ran on an Intel 7th generation core i5-7200U dual-core processor with 8GB of RAM. |
| Software Dependencies | No | The paper states: 'The algorithms were implemented in Python 3.6.7'. It does not list specific libraries or specialized solvers with version numbers. |
| Experiment Setup | Yes | We set n to 1000 and varied k from 27 = 128 to 216 = 65536. We then randomly flipped between 5% to 50% of the points around 0.5, and these points act as the outliers. The results are averaged over 10 independent trials. |