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..
Accelerating Greedy Coordinate Descent Methods
Authors: Haihao Lu, Robert Freund, Vahab Mirrokni
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we present results of our numerical experiments using AGCD and ASCD on synthetic linear regression problems as well as practical logistic regression problems. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics and Operations Research Center, MIT 2Sloan School of Management, MIT 3Google Research. |
| Pseudocode | Yes | Algorithm 1 Accelerated Coordinate Descent Framework without Strong Convexity |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Using this strategy, we solved a large number of logistic regression instances from LIBSVM (Chang & Lin, 2011). |
| Dataset Splits | No | The paper uses datasets like 'synthetic linear regression problems' and 'logistic regression instances from LIBSVM' but does not specify the train/validation/test splits, percentages, or sample counts used for experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using LIBSVM but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | No | The paper states that 'algorithm implementation details are described in the supplementary materials', but the main text does not include specific hyperparameters, optimizer settings, or other detailed experimental setup information. |