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..
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Authors: Florian Kalinke, Zoltan Szabo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we prove that the minimax optimal rate of HSIC estimation on Rd for Borel measures containing the Gaussians with continuous bounded translation-invariant characteristic kernels is O n 1/2 . |
| Researcher Affiliation | Academia | Florian Kalinke Institute for Program Structures and Data Organization Karlsruhe Institute of Technology Karlsruhe, Germany EMAIL Zoltán Szabó Department of Statistics London School of Economics London, UK EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for its methodology. |
| Open Datasets | No | The paper does not include experiments and thus does not use a training dataset. It focuses on theoretical bounds for distributions. |
| Dataset Splits | No | The paper does not include experiments and thus does not specify validation dataset splits. |
| Hardware Specification | No | The paper does not include experiments and thus does not provide hardware specifications. |
| Software Dependencies | No | The paper does not include experiments and thus does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not include experiments and thus does not provide details about an experimental setup like hyperparameters or training settings. |