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..
Adaptive Gradient-Based Meta-Learning Methods
Authors: Mikhail Khodak, Maria-Florina F. Balcan, Ameet S. Talwalkar
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Empirical Results: Adaptive Methods for Few-Shot & Federated Learning |
| Researcher Affiliation | Collaboration | Mikhail Khodak Carnegie Mellon University EMAIL Maria-Florina Balcan Carnegie Mellon University EMAIL Ameet Talwalkar Carnegie Mellon University & Determined AI EMAIL |
| Pseudocode | Yes | Algorithm 1: Generic online algorithm for gradient-based parameter-transfer meta-learning. and Algorithm 2: ARUBA: an approach for modifying a generic batch GBML method to learn a per-coordinate learning rate. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | 20-way Omniglot [37] 5-way Mini-Image Net [46] and Shakespeare dataset [12]. These are standard, publicly available datasets with proper citations. |
| Dataset Splits | No | The paper mentions using standard benchmarks like Omniglot and Mini-Image Net, but it does not explicitly specify train/validation/test split percentages, sample counts, or refer to a specific publication detailing the splits for these experiments. |
| Hardware Specification | No | The paper does not specify any particular CPU or GPU models, or other hardware components used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like Adam [36] and Fed Avg [41] but does not list any specific software dependencies or libraries with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions) required for reproduction. |
| Experiment Setup | Yes | Algorithm 2: ARUBA: an approach for modifying a generic batch GBML method to learn a per-coordinate learning rate. Input: T tasks, update method for meta-initialization, within-task descent method, settings ε, ζ, p > 0 Initialize b1 ε21d, g1 ζ21d |