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].
A Survey of Learning Criteria Going Beyond the Usual Risk
Authors: Matthew J. Holland, Kazuki Tanabe
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we survey and introduce a wide variety of non-traditional criteria used to design and evaluate machine learning algorithms, place the classical paradigm within the proper historical context, and propose a view of learning problems which emphasizes the question of what makes for a desirable loss distribution? in place of tacit use of the expected loss. |
| Researcher Affiliation | Academia | Matthew J. Holland EMAIL Kazuki Tanabe EMAIL Osaka University Ibaraki, Osaka 567-0047 Japan |
| Pseudocode | No | The paper discusses algorithmic approaches, but it does not contain any explicit pseudocode or algorithm blocks with structured steps. It focuses on theoretical criteria and their properties rather than implementation details. |
| Open Source Code | No | The paper is a survey of learning criteria and does not present new algorithms or empirical results requiring code release. There is no mention of open-source code or links to repositories for any methodology described in this paper. |
| Open Datasets | No | This paper is a theoretical survey and does not report on experiments using specific datasets. Therefore, no information about publicly available or open datasets is provided. |
| Dataset Splits | No | This paper is a theoretical survey and does not report on experiments. Thus, no information about training/test/validation dataset splits is provided. |
| Hardware Specification | No | This paper is a theoretical survey and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is a theoretical survey and does not involve experimental implementation details. Consequently, it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is a theoretical survey and does not include any experimental setup details, hyperparameters, or training configurations. |