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..
Dataset Meta-Learning from Kernel Ridge-Regression
Authors: Timothy Nguyen, Zhourong Chen, Jaehoon Lee
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform three sets of experiments to validate the efficacy of KIP and LS for dataset learning. We focus on MNIST (Le Cun et al., 2010) and CIFAR-10 (Krizhevsky et al., 2009) datasets for comparison to previous methods. |
| Researcher Affiliation | Industry | Timothy Nguyen Zhourong Chen Jaehoon Lee Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Kernel Inducing Point (KIP ) |
| Open Source Code | Yes | We provide an open source implementation of KIP and LS , available in an interactive Colab notebook1. 1https://colab.research.google.com/github/google-research/google-research/blob/master/kip/KIP.ipynb |
| Open Datasets | Yes | We focus on MNIST (Le Cun et al., 2010) and CIFAR-10 (Krizhevsky et al., 2009) datasets for comparison to previous methods. |
| Dataset Splits | No | We could have used a validation dataset for a stopping criterion, but that would have required reducing the target dataset from the entire training dataset. |
| Hardware Specification | Yes | using a single V100 GPU with 16GB of RAM |
| Software Dependencies | No | All our kernel-based experiments use the Neural Tangents library (Novak et al., 2020), built on top of JAX (Bradbury et al., 2018). |
| Experiment Setup | Yes | In all KIP trainings, we used the Adam optimizer. All our labels are mean-centered 1-hot labels. We used learning rates 0.01 and 0.04 for the MNIST and CIFAR-10 datasets, respectively. When sampling target batches, we always do so in a class-balanced way. All datasets are preprocessed using channel-wise standardization (i.e. mean subtraction and division by standard-deviation). For neural (tangent) kernels, we always use weight and bias variance σ2 w = 2 and σ2 b = 10 4, respectively. For both neural kernels and neural networks, we always use Re LU activation. Convolutional layers all use a (3, 3) filter with stride 1 and same padding. We train KIP for 10-20k iterations and took 5 random subsets of images for initializations. |