Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | LLM Run Details | Input Tokens: 42,567 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,304 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |