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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Instance Learning with Distribution Change
Authors: Wei-Jia Zhang, Zhi-Hua Zhou
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that MICS is almost always significantly better than many state-of-the-art multi-instance learning algorithms when distribution change occurs; and even when there is no distribution change, their performances are still comparable. |
| Researcher Affiliation | Academia | Wei-Jia Zhang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | No | The paper describes the approach mathematically and textually but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | First, we perform experiments on text data sets based on the 20 Newsgroups corpus popularly used in text categorization. |
| Dataset Splits | Yes | During the experiments, the parameters are selected via 5-folds cross validation on the training data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions) used for the implementation or experiments. |
| Experiment Setup | Yes | During the experiments, the parameters are selected via 5-folds cross validation on the training data. |