Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Understanding Benign Overfitting in Gradient-Based Meta Learning
Authors: Lisha Chen, Songtao Lu, Tianyi Chen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our analysis uses the relatively tractable linear models, our theory contributes to understanding the delicate interplay among data heterogeneity, model adaptation and benign overfitting in gradient-based meta learning tasks. We corroborate our theoretical claims through numerical simulations. |
| Researcher Affiliation | Collaboration | Lisha Chen Rensselaer Polytechnic Institute Troy, NY, USA EMAIL Songtao Lu IBM Research Yorktown Heights, NY, USA EMAIL Tianyi Chen Rensselaer Polytechnic Institute Troy, NY, USA EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] Work of theoretical nature. |
| Open Datasets | No | The paper discusses 'numerical simulations' based on a 'meta linear regression model' with 'Assumptions 2-4' about data properties, but does not specify a publicly available dataset used for these simulations. |
| Dataset Splits | Yes | For each task m, we observe N samples with input feature xm Xm Rd and target label ym Ym R drawn i.i.d. from a task-specific data distribution Pm. These samples are collected in the dataset Dm = {(xm,n, ym,n)}N n=1, which is divided into the train and validation datasets, denoted as Dtr m and Dva m. And |Dtr m| = Ntr and |Dva m| = Nva with N = Ntr + Nva. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the numerical simulations. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers used for the numerical simulations. |
| Experiment Setup | Yes | Figure 3: Excess risk vs number of samples (N) with different hyperparameters (M = 10, d = 200). Example 1 (Data covariance): Suppose Qm = diag(Id1, βId d1), m. Set M = 10, d = 200, d1 = 20, α = 0.1 for MAML and γ = 103 for i MAML. |