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..
$H$-Consistency Bounds: Characterization and Extensions
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides both a general characterization and an extension of H-consistency bounds for multi-class classification. We present new and tight H-consistency bounds for both the family of constrained losses and that of comp-sum losses... Our characterizations are based on error transformations... |
| 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 contains mathematical theorems, definitions, and proofs, but no explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments requiring dataset validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not describe an experimental setup requiring specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |