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 Meta-Learning
Authors: Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on three different largescale problems suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches. |
| Researcher Affiliation | Academia | 1UC Berkeley 2University of Washington. Correspondence to: Chelsea Finn <EMAIL>, Aravind Rajeswaran <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Online Meta-Learning with FTML |
| 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 | The experiments involve vision-based sequential learning tasks with the MNIST, CIFAR-100, and PASCAL 3D+ datasets. |
| Dataset Splits | No | The paper mentions 'held-out data Dtest_t' for evaluation and 'meta-training tasks' but does not specify explicit percentages or sample counts for train/validation/test splits, nor does it explicitly detail a 'validation' set split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory, or specific computing platforms) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam (Kingma & Ba, 2015)', 'standard automatic differentiation libraries', and the 'Mu Jo Co physics engine (Todorov et al., 2012)' but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For Rainbow MNIST, 'we set 90% classification accuracy as the proficiency threshold.' For pose prediction, 'set the proficiency threshold to an error of 0.05.' It also mentions 'Hyperparameters parameters ,', and that 'we meta-train with update minibatches of size at-most 25'. |