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..
Online Constrained Meta-Learning: Provable Guarantees for Generalization
Authors: Siyuan Xu, Minghui Zhu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we provide a practical algorithm for the framework and validate its superior effectiveness through experiments conducted on meta-imitation learning and few-shot image classification. |
| Researcher Affiliation | Academia | Siyuan Xu & Minghui Zhu School of Electrical Engineering and Computer Science The Pennsylvania State University University Park, PA 16801 EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Constrained Meta-Learning Framework |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We test the algorithms on two few-shot learning datasets, CUB [53] and mini-Image Net [52]. |
| Dataset Splits | No | The paper describes the number of data samples used for training and validation for each task (e.g., '|Dtr 0 | = 50', '|Dval 0 | = 50'), but it does not specify fixed training/validation/test splits (e.g., 80/10/10 percentages or specific counts) for the overall datasets that are needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper mentions software like PyTorch and optimizers like Adam, but it does not provide specific version numbers for these software components, which are necessary for reproducible descriptions. |
| Experiment Setup | Yes | In the experiments, the total number of tasks is T = 100. For each task, the number of training data |Dtr 0 | = 50, |Dtr + | = 50, and the validation data |Dval 0 | = 50. The regularization parameter λ = 0.1, and the perturbation parameter η = 0.01. ... For Few-shot image classification, the total number of tasks is T = 200. We consider 5-way 1-shot and 5-way 5-shot learning. The training data |Dtr 0 | = 5 and |Dtr + | = 5 for 5-shot learning, and |Dtr 0 | = 1 and |Dtr + | = 1 for 1-shot learning. The validation data |Dval 0 | = 50. |