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..
A Universal Growth Rate for Learning with Smooth Surrogate Losses
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 44,778 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,595 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 | This paper presents a comprehensive analysis of the growth rate of H-consistency bounds (and excess error bounds) for various surrogate losses used in classification. We prove a square-root growth rate near zero for smooth margin-based surrogate losses in binary classification, providing both upper and lower bounds under mild assumptions. This result also translates to excess error bounds. |
| Researcher Affiliation | Collaboration | Anqi Mao Courant Institute New York, NY 10012 EMAIL Mehryar Mohri Google Research & CIMS New York, NY 10011 EMAIL Yutao Zhong Courant Institute New York, NY 10012 EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. As no experiments are conducted, no dataset is used for training. |
| Dataset Splits | No | The paper does not include experiments. As no experiments are conducted, no dataset split information is provided. |
| Hardware Specification | No | The paper does not include experiments. As no experiments are conducted, no hardware specifications are provided. |
| Software Dependencies | No | The paper does not include experiments. As no experiments are conducted, no specific software dependencies are provided. |
| Experiment Setup | No | The paper does not include experiments. As no experiments are conducted, no experimental setup details are provided. |