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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Consistency of Kernel Methods with Dependent Observations
Authors: Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose the new notion of empirical weak convergence (EWC) as a general assumption explaining such phenomena for kernel methods. Our main results then establish consistency of SVMs, kernel mean embeddings, and general Hilbert-space valued empirical expectations with EWC data. Our analysis holds for both finite- and infinite-dimensional outputs, as we extend classical results of statistical learning to the latter case. Overall, our results open new classes of processes to statistical learning and can serve as a foundation for a theory of learning beyond i.i.d. and mixing. |
| Researcher Affiliation | Academia | 1Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany. |
| Pseudocode | No | The paper focuses on theoretical derivations and proofs, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, so no training/validation/test splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental setup, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experimental setup, thus no software dependencies are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |