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..
On the Stability and Generalization of Meta-Learning
Authors: Yunjuan Wang, Raman Arora
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We focus on developing a theoretical understanding of meta-learning... We introduce a novel notion of stability for meta-learning algorithms... and give explicit generalization bounds... We also conduct a simple experiment to empirically verify our generalization bounds... We report the transfer risk, the average empirical risk (over tasks), and the generalization gap for different values of m and n in Figure 1. |
| Researcher Affiliation | Academia | Yunjuan Wang Department of Computer Science Johns Hopkins University Baltimore, MD, 21218 EMAIL Raman Arora Department of Computer Science Johns Hopkins University Baltimore, MD, 21218 EMAIL |
| Pseudocode | Yes | Algorithm 1 Prox Meta-Learning Algorithm A, Algorithm 2 Task-specific Algorithm Atask, Algorithm 3 Stochastic Prox Meta-Learning |
| Open Source Code | Yes | The code is provided in the supplementary file. (Neur IPS Paper Checklist - E. Open access to data and code) |
| Open Datasets | No | The paper uses a synthetic one-dimensional sine wave regression problem where data is generated by the authors based on a function f(x; α, β) = α sin(x + β) with parameters sampled from uniform distributions. No concrete access information (link, DOI, repository, or citation to an external public dataset) is provided for the generated data itself. |
| Dataset Splits | No | The paper describes how training tasks (m tasks, each with n samples) and test tasks (new unseen tasks with n samples) are generated. It also mentions an 'evaluation set of size 200' for test tasks. However, it does not explicitly state a separate 'validation' split or how hyperparameters were tuned using such a split. |
| Hardware Specification | Yes | The experiment is conducted on a T4 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It does not mention libraries, frameworks, or solvers with their corresponding versions. |
| Experiment Setup | Yes | We run Algorithm 1 for T = 100 iterations with a step size of γ = 0.1 and regularization parameter λ = 0.5. Algorithm 2 (GD) is run for K = 15 iterations with step size η = 0.02. |