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 Fourier Approach to Mixture Learning
Authors: Mingda Qiao, Guru Guruganesh, Ankit Rawat, Kumar Avinava Dubey, Manzil Zaheer
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | If you ran experiments...(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Researcher Affiliation | Collaboration | Mingda Qiao Stanford University EMAIL Guru Guruganesh Google Research EMAIL Ankit Singh Rawat Google Research EMAIL Avinava Dubey Google Research EMAIL Manzil Zaheer Google Deep Mind EMAIL |
| Pseudocode | Yes | The pseudocode of our algorithms are presented in Appendix A. |
| Open Source Code | No | If you are including theoretical results...(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not report on empirical experiments that would involve training on datasets. The ethics statement explicitly notes 'If you ran experiments... [N/A]' for code and data. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, thus it does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments that would require hardware. The ethics statement explicitly notes 'If you ran experiments... [N/A]' for compute resources. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments that would require listing specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe a concrete experimental setup with hyperparameters or system-level training settings for empirical evaluation. |