On the Stability and Generalization of Meta-Learning
Authors: Yunjuan Wang, Raman Arora
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 ywang509@jhu.edu Raman Arora Department of Computer Science Johns Hopkins University Baltimore, MD, 21218 arora@cs.jhu.edu |
| 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. |