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..
SQ Lower Bounds for Learning Single Neurons with Massart Noise
Authors: Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is pure theoretical in nature. For a range of activation functions, including Re LUs, we establish superpolynomial Statistical Query (SQ) lower bounds for this learning problem. |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Lisheng Ren University of Wisconsin-Madison EMAIL Yuxin Sun University of Wisconsin-Madison EMAIL |
| Pseudocode | No | The paper describes its constructions and mathematical procedures using prose and definitions but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for its methodology, nor does it provide a link to a code repository. The ethics checklist marks N/A for code. |
| Open Datasets | No | The paper is theoretical and focuses on lower bounds for learning problems with statistical queries; it does not use or refer to any publicly available or open datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments that require dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |