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 Short Survey on Importance Weighting for Machine Learning
Authors: Masanari Kimura, Hideitsu Hino
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A Short Survey on Importance Weighting for Machine Learning. This survey summarizes the broad applications of importance weighting in machine learning and related research. |
| Researcher Affiliation | Academia | Masanari Kimura EMAIL School of Mathematics and Statistics The University of Melbourne Hideitsu Hino EMAIL The Institute of Statistical Mathematics Center for Advanced Intelligence Project, RIKEN |
| Pseudocode | No | The paper describes various algorithms and methods conceptually (e.g., IWERM, AIWERM, RIWERM, IWAL, Focal Loss, UMIX), often with mathematical formulations and descriptions of their steps, but it does not contain any clearly structured pseudocode blocks or algorithm listings in the main text. |
| Open Source Code | No | This paper is a survey and does not describe new methodology for which open-source code would typically be provided by the authors. There are no statements in the paper indicating the release of source code for the survey itself or any new methods described within it. |
| Open Datasets | No | This paper is a survey and does not conduct its own experiments. While it references various established datasets (e.g., CIFAR-10, ImageNet) in the context of discussing other research, it does not provide concrete access information or use them in its own experimental analysis. |
| Dataset Splits | No | This paper is a survey and does not conduct its own experiments. Therefore, it does not describe specific training/test/validation dataset splits used for its own work. |
| Hardware Specification | No | This paper is a survey and does not describe its own experimental execution. Therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | This paper is a survey and does not describe its own experimental implementation. Therefore, it does not list any specific software dependencies or version numbers. |
| Experiment Setup | No | This paper is a survey and does not describe its own experimental setup, hyperparameters, or training settings. |