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..
Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise
Authors: Shiwei Zeng, Jie Shen
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a new algorithm that runs in time (nd/ϵ)O(d) and under the Gaussian marginal distribution, PAC learns the class up to error rate ϵ with O( K4d ϵ2d log5d n) samples even when an η O(ϵd) fraction of them are corrupted by the nasty noise of Bshouty et al. (2002), possibly the strongest corruption model. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA. |
| Pseudocode | Yes | Algorithm 1 Main Algorithm: Attribute-Efficient Robust Chow Vector Estimator Algorithm 2 SPARSEFILTER |
| Open Source Code | No | The paper does not mention providing open-source code for its methodology. It refers to 'open-source code' in related works section regarding prior research, but not its own contribution. |
| Open Datasets | No | The paper is theoretical and does not describe actual experimental training or refer to specific datasets for this purpose. |
| Dataset Splits | No | The paper is theoretical and does not describe actual experimental validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe running experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe running experiments or specific software implementations with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |