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].
Learnability of the Superset Label Learning Problem
Authors: Liping Liu, Thomas Dietterich
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we analyze Empirical Risk Minimizing learners that use the superset error as the empirical risk measure. SLL data can arise either in the form of independent instances or as multiple-instance bags. For both scenarios, we give the conditions for ERM learnability and sample complexity for the realizable case. |
| Researcher Affiliation | Academia | Li-Ping Liu EMAIL Thomas G. Dietterich EMAIL School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and describes abstract |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments using real-world data, therefore no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any experimental setups or computational hardware used. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies with specific version numbers, as it does not involve implementation or experimentation. |
| Experiment Setup | No | The paper is entirely theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |