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..
Online Multitask Relative Similarity Learning
Authors: Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present our empirical studies on two real datatsets for evaluating both the efficacy and efficiency of the proposed algorithms. |
| Researcher Affiliation | Collaboration | 1Institute of High Performance Computing, A*STAR, Singapore 2Articial Intelligence Department, Ant Financial Services Group, China 3Institute for Infocomm Research, A*STAR, Singapore 4School of Information Systems, SMU, Singapore 5School of Computer Science and Engineering, NTU, Singapore |
| Pseudocode | Yes | Algorithm 1 OMTRSL: The proposed algorithm for Online Multi-Task Relative Similarity Learning. Algorithm 2 OMTRSL-Active: The proposed algorithm for Active Online Multi-Task Relative Similarity Learning. |
| Open Source Code | No | The paper mentions a link for a comparison algorithm (mt LMNN) but does not provide any concrete access or explicit statement about releasing its own source code for OMTRSL or OMTRSL-Active. |
| Open Datasets | Yes | The performances of the proposed methods are evaluated on two real-world datasets: the Isolet spoken alphabet recognition dataset [Fanty and Cole, 1990] and the news20 dataset 1. 1Available on the LIBSVM Machine Learning Repository. |
| Dataset Splits | Yes | On both datasets, we used standard 5-fold cross validation for evaluation, in which 80% of the data are used for training, and the remaining 20% are used for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It only mentions implementation language (Matlab). |
| Software Dependencies | No | The paper states "we implement our algorithms with pure Matlab language" but does not specify a version number for Matlab or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Input: Parameters C > 0 and b > 0 (for OMTRSL and OMTRSL-Active). Input: Parameters C > 0, b > 0 and δ > 0 (for OMTRSL-Active). An empirical setting of C is 1. |