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..
Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise
Authors: Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As our main contribution, we show that the complexity of MLC with RCN becomes drastically different in the presence of three or more labels. Specifically, we prove super-polynomial Statistical Query (SQ) lower bounds for this problem. In more detail, even for three labels and constant separation, we give a super-polynomial lower bound on the complexity of any SQ algorithm achieving optimal error. |
| Researcher Affiliation | Academia | 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, USA 2University of Athens and Archimedes AI, Athens, Greece. Correspondence to: Mingchen Ma <EMAIL>, Lisheng Ren <EMAIL>. |
| Pseudocode | No | The paper describes methods and proofs using mathematical notation but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | This paper is theoretical and focuses on proving complexity lower bounds. It does not conduct experiments on specific datasets, therefore no datasets are mentioned as publicly available. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental evaluation on datasets, so there is no mention of dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not involve running experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not involve implementation or execution of code for experiments, so no specific software dependencies or versions are listed. |
| Experiment Setup | No | This paper is theoretical and focuses on mathematical proofs and lower bounds rather than empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |