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 [1].
Orlicz Random Fourier Features
Authors: Linda Chamakh, Emmanuel Gobet, Zoltán Szabó
JMLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To tackle this difficulty, we establish a finite-sample deviation bound for a general class of polynomial-growth functions under α-exponential Orlicz condition on the distribution of the sample. Instantiating this result for RFFs, our finite-sample uniform guarantee implies a.s. convergence with tight rate for arbitrary kernel with α-exponential Orlicz spectrum and any order of derivative. |
| Researcher Affiliation | Collaboration | Linda Chamakh EMAIL Emmanuel Gobet EMAIL Zolt an Szab o EMAIL CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris 91128 Palaiseau, France Global Markets Quantitative Research BNP Paribas |
| Pseudocode | No | The paper describes mathematical proofs and theoretical analysis but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about providing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper presents theoretical research, focusing on mathematical bounds and convergence rates for Orlicz Random Fourier Features, and does not involve experiments on specific datasets. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using datasets, so there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and analysis; no computational experiments requiring specific hardware are described. |
| Software Dependencies | No | The paper is purely theoretical, providing mathematical proofs and analysis. It does not mention any software or libraries with version numbers used for computational experiments. |
| Experiment Setup | No | The paper is theoretical, presenting mathematical analysis and proofs. There are no experimental setups, hyperparameters, or training configurations described. |