The Exact Sample Complexity Gain from Invariances for Kernel Regression
Authors: Behrooz Tahmasebi, Stefanie Jegelka
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 bzt@mit.edu Stefanie Jegelka MIT CSAIL and TU Munich stefje@mit.edu |
| 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. |