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 [1].
A Universal Growth Rate for Learning with Smooth Surrogate Losses
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2024 | Venue PDF | LLM Run Details
| 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. |