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].
Bounds for Learning from Evolutionary-Related Data in the Realizable Case
Authors: Ondřej Kuželka, Yuyi Wang, Jan Ramon
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the realizable case, we prove PAC-type upper and lower bounds based on symmetries and matchings in such trees. |
| Researcher Affiliation | Academia | Ondˇrej Kuˇzelka Cardiff University, UK Kuzelka EMAIL Yuyi Wang ETH, Switzerland EMAIL Jan Ramon INRIA, France/KU Leuven, Belgium EMAIL |
| Pseudocode | No | The paper contains mathematical proofs and definitions but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to code repositories. |
| Open Datasets | No | The paper is theoretical and models a learning task with 'samples' and 'examples' within its framework, but it does not specify or use any publicly available or open datasets for training experimental models. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no training/validation/test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe implementation details or computational experiments, therefore no specific software dependencies or versions are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments, therefore no experimental setup details such as hyperparameters or training configurations are provided. |