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.
Fine-grained Generalization Analysis of Structured Output Prediction
Authors: Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius Kloft
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,521 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,466 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 | 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. |