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..
Robust Regression of General ReLUs with Queries
Authors: Ilias Diakonikolas, Daniel Kane, Mingchen Ma
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper is theoretical in nature and does not include experiments. |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Mingchen Ma University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 QUERYLEARNING(Learn optimal Re LU with a warm start) ... Algorithm 2 HYPOTHESISSELECTION(Select a good hypothesis from a list of hypothesis) ... Algorithm 3 SPHERICALLEARNING(Learn w over the unit sphere) ... Algorithm 4 QUERYLEARNING(Learn optimal Re LU with a warm start) |
| Open Source Code | No | The paper is theoretical in nature and does not include code, data or experiments. |
| Open Datasets | No | The paper is theoretical in nature and does not include code, data or experiments. |
| Dataset Splits | No | The paper is theoretical in nature and does not include experiments. |
| Hardware Specification | No | The paper is theoretical in nature and does not include experiments. |
| Software Dependencies | No | The paper is theoretical in nature and does not include experiments. |
| Experiment Setup | No | The paper is theoretical in nature and does not include experiments. |