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..
Efficient Meta Learning via Minibatch Proximal Update
Authors: Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several few-shot regression and classification tasks demonstrate the advantages of our method over state-of-the-arts. |
| Researcher Affiliation | Collaboration | Learning & Vision Lab, National University of Singapore, Singapore B-DAT Lab, Nanjing University of Information Science & Technology, Nanjing, China Alibaba and Georgia Institute of Technology, USA YITU Technology, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 SGD for Meta-Minibatch Prox |
| Open Source Code | Yes | The code is available at https://panzhous.github.io. |
| Open Datasets | Yes | mini Image Net [5] and tiered Image Net [43] |
| Dataset Splits | Yes | Following [6, 10], we use the split proposed in [5], which consists of 64 classes for training, 16 classes for validation and the remaining 20 classes for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions optimizers like SGD and Adam, but does not provide specific version numbers for any software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For our Meta-Minibatch Prox, we set λ = 0.5 and use SGD to solve the inner subproblem with 15 steps of iteration with learning rate 0.02. For the learning rate ηs in Meta-Minibatch Prox, we decrease it at each iteration as ηs = α(1 s/S) where the total iteration number S in Algorithm 1 and α are set to 30, 000 and 0.8, respectively. |