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..
Strength from Weakness: Fast Learning Using Weak Supervision
Authors: Joshua Robinson, Stefanie Jegelka, Suvrit Sra
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are reflected empirically across a range of tasks and illustrate how weak labels speed up learning on the strong task. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology, Cambridge, MA 02139. Correspondence to: Joshua Robinson <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Pretrain-finetune meta-algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | For our CIFAR-10 experiments...fine tuning on a small subset CIFAR-100...Databases left to right: MNIST, SVHN, and CIFAR-10. ...TREC fast-based question categorization dataset. |
| Dataset Splits | No | The paper describes a 'held out dataset' for training an auxiliary network and discusses 'generalization error', but it does not specify concrete train/validation/test split percentages or sample counts for its experiments. |
| Hardware Specification | No | The paper mentions that 'All image-based experiments use either a Res Net-18 or Res Net-34 for the weak feature map g (see Appendix C for full details)', suggesting hardware details might be in the appendix, but it does not provide specific hardware models (e.g., GPU, CPU) in the provided text. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. While PyTorch is referenced, its version is not stated as a dependency for the authors' work. |
| Experiment Setup | No | The paper states 'All image-based experiments use either a Res Net-18 or Res Net-34 for the weak feature map g (see Appendix C for full details)', implying experimental setup details are in the appendix. However, no specific hyperparameters or training configurations are provided in the main text. |