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..
Theoretical Analysis of Label Distribution Learning
Authors: Jing Wang, Xin Geng5256-5263
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL. Firstly, risk bounds for three representative LDL algorithms (AA-k NN, AA-BP and SA-ME) are provided. As far as we know, this is perhaps the ο¬rst research on theory of LDL. |
| Researcher Affiliation | Academia | Jing Wang, Xin Geng MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering, Southeast University, Nanjing 210096, China EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured steps presented in a code-like format. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving specific datasets. It mentions a 'training set S' as part of its theoretical notation for analyzing LDL algorithms, but does not provide access information for any public dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe empirical experiments, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe the execution of experiments, therefore it does not provide any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe the setup of any empirical experiments, thus no hyperparameters or system-level training settings are provided. |