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 Exact Sample Complexity Gain from Invariances for Kernel Regression
Authors: Behrooz Tahmasebi, Stefanie Jegelka
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study this phenomenon from a theoretical perspective. In particular, we provide minimax optimal rates for kernel ridge regression on compact manifolds, with a target function that is invariant to a group action on the manifold. |
| Researcher Affiliation | Academia | Behrooz Tahmasebi MIT CSAIL EMAIL Stefanie Jegelka MIT CSAIL and TU Munich EMAIL |
| Pseudocode | No | The paper focuses on mathematical derivations and theoretical proofs, and does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not contain any statement about releasing source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper does not report empirical experiments and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on any experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on any experiments that would require specifying software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report on any experiments, thus no experimental setup details like hyperparameters or training settings are provided. |