Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Bound for Parameter Transfer Learning
Authors: Wataru Kumagai
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 14,746 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,823 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Then, we introduce the notion of the local stability and parameter transfer learnability of parametric feature mapping, and thereby derive a learning bound for parameter transfer algorithms. ... In this paper, we also provide the ο¬rst theoretical learning bound for self-taught learning. |
| Researcher Affiliation | Academia | Wataru Kumagai Faculty of Engineering Kanagawa University EMAIL |
| Pseudocode | No | The paper presents theoretical formulations, theorems, and proofs, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on deriving learning bounds; it does not describe experimental evaluation on a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |