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..
Label Noise SGD Provably Prefers Flat Global Minimizers
Authors: Alex Damian, Tengyu Ma, Jason D. Lee
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 presents experimental results which support our theory. Finally, Section 6 discusses the implications of this work. and 4 Experiments In order to test the ability of SGD with label noise to escape poor global minimizers and converge to better minimizers, we initialize Algorithm 1 at global minimizers of the training loss which achieve 100% training accuracy yet generalize poorly to the test set. |
| Researcher Affiliation | Academia | Alex Damian Princeton University EMAIL Tengyu Ma Stanford University EMAIL Jason Lee Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1: SGD with Label Noise Input: θ0, step size η, noise variance σ2, batch size B, steps T |
| Open Source Code | No | Code will be submitted through the supplementary material and will be made available (through Github) upon acceptance. |
| Open Datasets | Yes | Experiments were run with Res Net18 on CIFAR10 [17] without data augmentation or weight decay. For CIFAR10 we cite Krizhevsky [17], as requested by the creators on https://www.cs.toronto.edu/ kriz/cifar.html. |
| Dataset Splits | No | The paper mentions 'training accuracy' and 'test accuracy' in Section 4, but it does not specify the use of a separate validation split, its size, or how it was created. |
| Hardware Specification | Yes | The experiments were performed on NVIDIA GeForce RTX 2080 Ti GPUs. |
| Software Dependencies | No | The code was implemented in PyTorch [24] and PyTorch Lightning [6], and weights and biases [2] was used for experiment tracking. |
| Experiment Setup | Yes | Experiments were run with Res Net18 on CIFAR10 [17] without data augmentation or weight decay. The experiments were conducted with randomized label flipping with probability 0.2 (see Appendix E for the extension of Theorem 1 to classification with label flipping), cross entropy loss, and batch size 256. |