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..
Multi-Class Learning using Unlabeled Samples: Theory and Algorithm
Authors: Jian Li, Yong Liu, Rong Yin, Weiping Wang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Coinciding with the theoretical analysis, experimental results demonstrate that the stated approach achieves better performance. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences EMAIL |
| Pseudocode | Yes | Algorithm 1 Proximal Stochastic Sub-gradient Singular Value Thresholding (PS3VT) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its methodology's code. |
| Open Datasets | Yes | We run PS3VT and the compared methods on 15 multi-class datasets and report the results in Table 3. Labeled and unlabeled samples are given by stratified random sampling from train data that 30% as labeled samples and the rest as unlabeled ones. |
| Dataset Splits | Yes | For fair comparison, before a method runs on any dataset, we employ 5-folds cross validation to obtain the optimal parameter set by grid search over candidate sets complexity parameter τA {10 15, 10 14, , 10 6}, unlabeled samples parameter τI {0, 10 15, 10 14, , 10 6}, local Rademacher complexity parameter τS {0, 10 10, 10 9, , 10 1}, step size 1 µ {101, 102, , 105} and tail parameter θ {0.5, 0.6, , 0.9} min(|K|, |d|). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For fair comparison, before a method runs on any dataset, we employ 5-folds cross validation to obtain the optimal parameter set by grid search over candidate sets complexity parameter τA {10 15, 10 14, , 10 6}, unlabeled samples parameter τI {0, 10 15, 10 14, , 10 6}, local Rademacher complexity parameter τS {0, 10 10, 10 9, , 10 1}, step size 1 µ {101, 102, , 105} and tail parameter θ {0.5, 0.6, , 0.9} min(|K|, |d|). |