Preference Based Adaptation for Learning Objectives
Authors: Yao-Xiang Ding, Zhi-Hua Zhou
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 {dingyx, zhouzh}@lamda.nju.edu.cn |
| 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. |