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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination
Authors: Ilias Diakonikolas, Chao Gao, Daniel Kane, John D. Lafferty, Ankit Pensia
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 37,562 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,792 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Answer: [NA] Justification: No experimental results. |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin, Madison EMAIL Chao Gao University of Chicago EMAIL Daniel M. Kane University of California, San Diego EMAIL John Lafferty Yale University EMAIL Ankit Pensia Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Answer: [NA] Justification: No experiments. |
| Open Datasets | No | Answer: [NA] Justification: No experiments. |
| Dataset Splits | No | Answer: [NA] Justification: No experiments. |
| Hardware Specification | No | Answer: [NA] Justification: No experiments. |
| Software Dependencies | No | Answer: [NA] Justification: No experiments. |
| Experiment Setup | No | Answer: [NA] Justification: No experiments. |