$H$-Consistency Bounds: Characterization and Extensions
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 aqmao@cims.nyu.edu Mehryar Mohri Google Research & CIMS New York, NY 10011 mohri@google.com Yutao Zhong Courant Institute New York, NY 10012 yutao@cims.nyu.edu |
| 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. |