Fine-grained Generalization Analysis of Structured Output Prediction
Authors: Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius Kloft
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we significantly improve the state of the art by developing novel high-probability bounds with a logarithmic dependency on d. Moreover, we leverage the lens of algorithmic stability to develop generalization bounds in expectation without any dependency on d. Our results therefore build a solid theoretical foundation for learning in large-scale SOPPs. |
| Researcher Affiliation | Academia | Waleed Mustafa1 , Yunwen Lei2 , Antoine Ledent1 and Marius Kloft1 1TU Kaiserslautern 2 University of Birmingham |
| Pseudocode | No | The paper describes algorithms but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets for training experiments. It discusses data in abstract terms, e.g., 'Let S = {(xi, yi)}m i=1 be a training set with (xi, yi) X Y being independently drawn from a distribution D over X Y'. |
| Dataset Splits | No | The paper is theoretical and does not provide details about dataset splits (training, validation, test) for experimental reproduction. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about experimental setup, such as hyperparameters or training settings. |