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..
Simple and near-optimal algorithms for hidden stratification and multi-group learning
Authors: Christopher J Tosh, Daniel Hsu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the structure of solutions to the multi-group learning problem, and provides simple and near-optimal algorithms for the learning problem. All proofs are presented in the appendix. |
| Researcher Affiliation | Collaboration | 1Memorial Sloan Kettering Cancer Center, New York, NY 2Department of Computer Science, Columbia University, New York, NY. |
| Pseudocode | Yes | Algorithm 1 PREPEND, Algorithm 2 Reduction to sleeping experts, Algorithm 3 Consistent majority algorithm, Algorithm 4 MLC-HEDGE in the multi-group setting. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and discusses 'n i.i.d. training examples drawn from a distribution D' but does not specify or provide access information for any public dataset. |
| Dataset Splits | No | The paper is theoretical and does not discuss specific dataset splits (training, validation, test) needed for reproducibility. |
| Hardware Specification | No | The paper focuses on theoretical algorithms and proofs, and therefore does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes algorithms theoretically but does not provide specific experimental setup details like hyperparameter values or training configurations. |