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.
Benign overfitting in leaky ReLU networks with moderate input dimension
Authors: Kedar Karhadkar, Erin George, Michael Murray, Guido F. Montufar, Deanna Needell
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 36,414 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,478 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To further support our theory, we train shallow neural networks on the data model described in Definition 2.1 and record the numerical results. Scripts to reproduce these experiments can be found at https://github.com/kedar2/benign_overfitting. These experiments were run on the CPU of a Mac Book Pro M2 with 8GB of RAM. |
| Researcher Affiliation | Academia | Kedar Karhadkar1 Erin George1 Michael Murray1 Guido Montúfar12 Deanna Needell1 EMAIL 1UCLA 2Max Planck Institute for Mathematics in the Sciences Equal contribution |
| Pseudocode | No | The paper contains mathematical equations and descriptions of algorithms, but no structured pseudocode blocks or algorithm boxes are presented. |
| Open Source Code | Yes | Scripts to reproduce these experiments can be found at https://github.com/kedar2/benign_overfitting. |
| Open Datasets | No | 2.1 Data model We study data generated as per the following data model. Definition 2.1. Suppose d, n, k N, (0, 1) and v Sd 1. If (X, y, y, x, y) D(d, n, k, , v) then 1. X Rn d is a random matrix whose rows, which we denote xi, satisfy xi = yiv + 1 ni, where ni N(0d, 1 d(Id vv T )) are mutually i.i.d.. |
| Dataset Splits | Yes | Parameter settings: = 0.1, = 5/n, m = 64, k = 0.1n, number of trials = 5, size of validation sample = 1000. |
| Hardware Specification | Yes | These experiments were run on the CPU of a Mac Book Pro M2 with 8GB of RAM. |
| Software Dependencies | No | The paper describes the learning algorithm (gradient descent with hinge loss) but does not specify any particular software libraries or frameworks with version numbers (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | Parameter settings: = 0.1, = 5/n, m = 64, k = 0.1n, number of trials = 5, size of validation sample = 1000. |