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..
A General Analysis of Example-Selection for Stochastic Gradient Descent
Authors: Yucheng Lu, Si Yi Meng, Christopher De Sa
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate our two algorithms on several image classification benchmarks including MNIST, CIFAR10/100 and Image Net. We show with QMC-based data augmentation, a higher validation accuracy can be achieved without hyperparameter tuning this suggests that QMC may be a good default driver to use with data augmentation for deep learning in general. Meanwhile, the greedy algorithm converges faster both in terms of iteration and wall-clock time (Section 6). |
| Researcher Affiliation | Academia | Yucheng Lu , Si Yi Meng , Christopher De Sa Department of Computer Science Cornell University Ithaca, NY 14853, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Example-Ordered SGD via Greedily Minimizing Average Gradient Error |
| Open Source Code | Yes | Our code is available at: https://github.com/EugeneLYC/qmc-ordering. |
| Open Datasets | Yes | Empirically, we evaluate our two algorithms on several image classification benchmarks including MNIST, CIFAR10/100 and Image Net. ... In addition to using synthetic data, we also performed an offline version of the experiment Figure 1(b) on a real dataset, a6a from the LIBSVM repository (Chang & Lin, 2011). |
| Dataset Splits | Yes | Empirically, we evaluate our two algorithms on several image classification benchmarks including MNIST, CIFAR10/100 and Image Net. ... We start by training Res Net20 on CIFAR10 and CIFAR100, where discrete and continuous data augmentations are applied, respectively. |
| Hardware Specification | Yes | In Section 6, all the training scripts are implemented via Py Torch1.6 and run on a single machine configured with an 2.6GHz 4-core Intel (R) Xeon(R) CPU, 16GB memory and NVIDIA Ge Force GTX 1080Ti with CUDA 10.1. |
| Software Dependencies | Yes | In Section 6, all the training scripts are implemented via Py Torch1.6 and run on a single machine configured with an 2.6GHz 4-core Intel (R) Xeon(R) CPU, 16GB memory and NVIDIA Ge Force GTX 1080Ti with CUDA 10.1. |
| Experiment Setup | Yes | We run the baseline IID-uniform method with finetuned hyperparameters (weight decay 10 4), which reproduces the result from He et al. (2016) with an error rate 8.4%. Then we run QMC-base augmentation with the same hyperparameter (untuned) and finetuned counterparts, with a grid search over weight decay values in {r 10 4}4 r=1. |