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..
Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis
Authors: Jian Li, Yong Liu, Rong Yin, Weiping Wang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical results reveal that our method achieves the state-of-the-art performance in a short time even with limited computing resources. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 Nystr om Lap RLS with PCG (Nystr om-PCG) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions datasets like 'space ga', 'phishing', 'a8a', 'w7a', 'a9a', 'ijcnn1', 'cod-rna', 'connect-4', 'skin nonskin', 'Year Prediction' but does not provide specific links, DOIs, repositories, or formal citations (author and year) to confirm their public availability or how to access them. |
| Dataset Splits | Yes | Using the chosen parameters determined by 10-folds cross-validation, we run all methods 30 times with randomly select 70% for training and 30% for testing on each dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We choose kernel parameter σ and regular parameters (λ in standard RLS and λA, λI in Lap RLS methods) in 2i, i { 15, 14, , 14, 15}, by minimizing test error via 10-folds cross-validation. For each dataset, we use Gaussian kernel K(xi, xj) = exp( xi xj /2σ2). |