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 [1].
Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning
Authors: Peizhong Ju, Yingbin Liang, Ness Shroff
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an initial step towards addressing this challenge, this paper studies the generalization performance of overfitted meta-learning under a linear regression model with Gaussian features. ... With this upper bound and simulation results, we confirm the benign overfitting in meta-learning by comparing the model error of the overfitted solution with the underfitted solution. We further characterize some interesting properties of the descent curve. ... In Appendix E.2, we provide a further experiment where we train a two-layer fully connected neural network over the MNIST data set. |
| Researcher Affiliation | Academia | Peizhong Ju Department of ECE The Ohio State University Columbus, OH 43210, USA EMAIL Yingbin Liang Department of ECE The Ohio State University Columbus, OH 43210, USA EMAIL Ness B. Shroff Department of ECE & CSE The Ohio State University Columbus, OH 43210, USA EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not contain any statement about open-sourcing code or provide a link to a code repository. |
| Open Datasets | Yes | In this section, we further verify our theoretical findings by an experiment over a two-layer fully-connected neural network on the MNIST data set. |
| Dataset Splits | Yes | For each training task, there are 1000 training samples and 100 validation samples. ... The number of validation samples is nv = 100 for each of these 4 training tasks. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used for running experiments. |
| Software Dependencies | No | The paper mentions software components like "deep neural networks" but does not list specific software dependencies (e.g., libraries, frameworks) with version numbers required for replication. |
| Experiment Setup | Yes | The step size in the outer-loop training is 0.3 and the step size of the one-step gradient adaptation is \u03b1t = \u03b1r = 0.05. After training 500 epochs, the meta-training error for each simulation is lower than 0.025 (the range of the meta-training error is [0, 1]). |