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..
Multi-Class $H$-Consistency Bounds
Authors: Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an extensive study of H-consistency bounds for multi-class classification. We give a series of new H-consistency bounds for surrogate multi-class losses, including max losses, sum losses, and constrained losses, both in the non-adversarial and adversarial cases, and for different differentiable or convex auxiliary functions used. We also prove that no non-trivial H-consistency bound can be given in some cases. Our proof techniques are also novel and likely to be useful in the analysis of other such guarantees. |
| Researcher Affiliation | Collaboration | Pranjal Awasthi Google Research New York, NY 10011 EMAIL Anqi Mao Courant Institute New York, NY 10012 EMAIL Mehryar Mohri Google Research & Courant Institute New York, NY 10011 EMAIL Yutao Zhong Courant Institute New York, NY 10012 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not present any empirical studies or use datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameter values or training configurations. |