Multi-Class $H$-Consistency Bounds
Authors: Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 pranjalawasthi@google.com Anqi Mao Courant Institute New York, NY 10012 aqmao@cims.nyu.edu Mehryar Mohri Google Research & Courant Institute New York, NY 10011 mohri@google.com Yutao Zhong Courant Institute New York, NY 10012 yutao@cims.nyu.edu |
| 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. |