A Fourier Approach to Mixture Learning
Authors: Mingda Qiao, Guru Guruganesh, Ankit Rawat, Kumar Avinava Dubey, Manzil Zaheer
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 mqiao@stanford.edu Guru Guruganesh Google Research gurug@google.com Ankit Singh Rawat Google Research ankitsrawat@google.com Avinava Dubey Google Research avinavadubey@google.com Manzil Zaheer Google Deep Mind manzilzaheer@google.com |
| 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. |