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..
Promises and Pitfalls of Threshold-based Auto-labeling
Authors: Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical guarantees with extensive experiments on synthetic and real datasets1. |
| Researcher Affiliation | Academia | Harit Vishwakarma EMAIL University of Wisconsin-Madison Heguang Lin EMAIL University of Pennsylvania Frederic Sala EMAIL University of Wisconsin-Madison Ramya Korlakai Vinayak EMAIL University of Wisconsin-Madison |
| Pseudocode | Yes | Algorithm 1 Threshold-based Auto-Labeling (TBAL) Input: Unlabeled pool Xpool, auto labeling error threshold Ďľa, seed data size ns, batch size for active query nb, labeled validation data pool Dval. Output: Dout = {(xi, yi) : xi Xpool} |
| Open Source Code | Yes | 1https://github.com/harit7/TBAL |
| Open Datasets | Yes | We use the following synthetic and real datasets. [...] a) Unit-Ball [...] b) Tiny-Image Net [...] c) IMDB Reviews [...] d) CIFAR-10 [...] MNIST [15] is a standard image dataset of hand-written digits. |
| Dataset Splits | Yes | We split the data into two sufficiently large pools. One is used as Xpool on which auto-labeling algorithms are run and the other is used as Xval from which the algorithms subsample validation data. [...] Unit-Ball [...] 16K are in Xpool and 4K are in Xval. [...] IMDB Reviews [...] standard train set of size 25K and split it into Xpool and Xval of sizes 20K and 5K respectively. [...] CIFAR-10 [...] standard training set into Xpool of size 40K and the validation pool of size 10K. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processors, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'sklearn' for SVMs and 'PyTorch' in tutorials, but it does not specify exact version numbers for these or other software dependencies. |
| Experiment Setup | Yes | To train a multi-layer perceptron (MLP) on the pre-computed embeddings of IMDB and Tiny-Image Net we use SGD with a learning rate of 0.05, 0.1 respectively, and batch size of 64. To train the medium CNN we use SGD with a learning rate of 10 2, batch size 256, and momentum of 0.9. More details on model training are in the Appendix. |