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.
Simplicity Bias in Overparameterized Machine Learning
Authors: Yakir Berchenko
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 16,879 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,244 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 | Here we demonstrate that simplicity bias is a major phenomenon to be reckoned with in overparameterized machine learning. In addition to explaining the outcome of simplicity bias, we also study its source: following concrete rigorous examples, we argue that... |
| Researcher Affiliation | Academia | Department of Industrial Engineering and Management, Ben-Gurion University of the Negev P. O. 653, Beer-Sheva 84105, Israel. EMAIL |
| Pseudocode | No | The paper describes "Naive Algorithm" in numbered steps within paragraph text but does not provide formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for its described methodologies. |
| Open Datasets | No | The paper uses abstract examples for theoretical analysis (e.g., "Boolean functions on n variables," "black/white images with n 28 ˆ 28 784 pixels") but does not provide access information (link, DOI, or specific citation) for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental dataset splits (training, validation, test) or cross-validation setups. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations for an empirical study. |