Finding One’s Best Crowd: Online Learning By Exploiting Source Similarity
Authors: Yang Liu, Mingyan Liu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To our best knowledge this is the first study on online learning by exploiting source similarity with provable performance guarantees. Unless otherwise specified, all proofs can be found in (Liu and Liu 2015). |
| Researcher Affiliation | Academia | Yang Liu and Mingyan Liu Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor 1301 Beal Avenue, Ann Arbor, Michigan 48109 {youngliu,mingyan}@umich.edu |
| Pseudocode | Yes | Algorithm 1 K-Learning |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific, named public datasets for which access information would be provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with training, validation, and test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers required for reproducing empirical results. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |