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 [1].
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. |