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..
Classification from Pairwise Similarity and Unlabeled Data
Authors: Han Bao, Gang Niu, Masashi Sugiyama
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of the proposed method through experiments.In this section, we empirically investigate the performance of class-prior estimation and the proposed method for SU classification. |
| Researcher Affiliation | Academia | 1The University of Tokyo, Japan 2RIKEN, Japan. |
| Pseudocode | Yes | Algorithm 1 Prior estimation from SU data. CPE is a classprior estimation algorithm. |
| Open Source Code | Yes | Our implementation is available at https://github.com/levelfour/SU_Classification. |
| Open Datasets | Yes | Datasets: Datasets are obtained from the UCI Machine Learning Repository (Lichman, 2013), the LIBSVM (Chang & Lin, 2011), and the ELENA project 5. |
| Dataset Splits | Yes | To choose hyperparameters, 5-fold cross-validation is used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like LIBSVM and KM2, but does not list specific software dependencies with version numbers for its own implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The regularization parameter λ is chosen from {10 1, 10 4, 10 7}.To choose hyperparameters, 5-fold cross-validation is used. |