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..
Preference Based Adaptation for Learning Objectives
Authors: Yao-Xiang Ding, Zhi-Hua Zhou
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the overall approach to multi-label learning, and show that the proposed approach achieves significant performance under various multi-label performance measures. |
| Researcher Affiliation | Academia | Yao-Xiang Ding Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Dueling bandit Learning for Logit Model (DL2M) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The experiments are conducted on six benchmark multi-label datasets 1: emotions, CAL500, enron, Corel5k, medical and bibtex. (Footnote 1: http://mulan.sourceforge.net/datasets-mlc.html) |
| Dataset Splits | Yes | To implement DL2M , each dataset is randomly split into training, validation and testing set, with ratio of size 3:1:1. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | During the learning process, the preference feedback is generated by testing the learned hypothesis on the validation set, and DL2M is utilized to update the objective for 20 iterations, with c = 0.05, λ = 1. |