Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
Authors: Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper is theoretical in nature and does not include experiments. |
| Researcher Affiliation | Academia | Shuyao Li University of Wisconsin-Madison EMAIL Sushrut Karmalkar University of Wisconsin-Madison EMAIL Ilias Diakonikolas University of Wisconsin-Madison EMAIL Jelena Diakonikolas University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1: Main algorithm |
| Open Source Code | No | The paper is theoretical in nature and does not include experiments. Therefore, no open-source code for the methodology is provided. |
| Open Datasets | No | The paper is theoretical in nature and does not include experiments, and therefore does not refer to specific datasets or their public availability. |
| Dataset Splits | No | The paper is theoretical in nature and does not include experiments, and therefore does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical in nature and does not include experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical in nature and does not include experiments; therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical in nature and does not include experiments; therefore, no specific experimental setup details, such as hyperparameters or training settings, are provided. |