Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Authors: John C. Duchi, Vitaly Feldman, Lunjia Hu, Kunal Talwar
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove an upper bound for the estimation error of our estimator in general scenarios where the number of data points and amount of noise can vary across users, and prove an information-theoretic error lower bound that not only matches the upper bound up to a constant factor, but also holds even for spherical Gaussian noise. |
| Researcher Affiliation | Collaboration | John Duchi Stanford University jduchi@stanford.edu Vitaly Feldman Apple vitaly.edu@gmail.com Lunjia Hu Stanford University lunjia@stanford.edu Kunal Talwar Apple kunal@kunaltalwar.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with a training dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments requiring validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |