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].
Convex Calibration Dimension for Multiclass Loss Matrices
Authors: Harish G. Ramaswamy, Shivani Agarwal
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study consistency properties of surrogate loss functions for general multiclass learning problems, defined by a general multiclass loss matrix. We extend the notion of classification calibration... We then introduce the notion of convex calibration dimension of a multiclass loss matrix... We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, we apply our framework to study various subset ranking losses, and use the convex calibration dimension as a tool to show both the existence and non-existence of various types of convex calibrated surrogates for these losses. |
| Researcher Affiliation | Academia | Harish G. Ramaswamy harish EMAIL Shivani Agarwal EMAIL Department of Computer Science and Automation Indian Institute of Science Bangalore 560012, India |
| Pseudocode | No | The paper describes methodologies and theoretical derivations using mathematical notation and narrative text. It does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to code repositories. |
| Open Datasets | No | The paper focuses on theoretical properties of loss matrices and does not conduct experiments on datasets, thus no dataset availability information is provided. |
| Dataset Splits | No | The paper focuses on theoretical properties of loss matrices and does not conduct experiments on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical work and does not report on experimental results that would require specific hardware for computation. |
| Software Dependencies | No | The paper describes theoretical work and does not report on experimental results that would require specific software dependencies for implementation. |
| Experiment Setup | No | The paper presents theoretical analysis and mathematical derivations rather than empirical experiments, therefore no experimental setup details or hyperparameters are discussed. |