Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes
Authors: Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl Motahari, Babak Khalaj
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our proofs are a combination of the so-called sample compression technique from (Ashtiani et al., 2018), mathematical tools from high-dimensional geometry, and Fourier analysis. In particular, we have proposed a general Fourier-based technique for recovery of a more general class of distribution families from additive Gaussian noise, which can be further used in a variety of other related problems. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran. 2School of Mathematics, Institute for Research in Fundamental Sciences (IPM), P.O. Box: 19395-5746, Tehran, Iran. 3Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. |
| Pseudocode | No | The paper describes a conceptual algorithm and steps, but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described methods. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets, so no dataset information or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus there is no discussion of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no specific experimental setup details like hyperparameters or training configurations are provided. |