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..
Convex Co-Embedding for Matrix Completion with Predictive Side Information
Authors: Yuhong Guo
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on two types of applications, transductive multi-label learning with incomplete labels and recommendation matrix completion. In this section we present the experimental settings and results. |
| Researcher Affiliation | Academia | Yuhong Guo School of Computer Science Carleton University, Ottawa, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 Fast Proximal Gradient Descent Algorithm |
| Open Source Code | No | The paper does not provide any statement regarding the release of source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We conducted experiments for transductive incomplete multi-label learning on ten standard multi-label datasets for web page classification from yahoo.com (Ueda and Saito 2002). We have also conducted recommendation experiments on three real-word Amazon datasets: Beauty, Office and Sports. |
| Dataset Splits | Yes | For each dataset, we randomly sampled 10% instances for testing and used the remaining 90% data for training. We did parameter selection for the regularization parameter γ from the set 2{ 9, 8, ,8,9} by using two-fold cross-validation on the labeled training data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The proposed approach, Co Embed, has three trade-off parameters α, γ and ρ. Note as shown in Proposition 1, any difference between α and γ will simply lead to rescaling the input feature values. Hence we just set α = γ. Moreover, the ρ parameter controls the degree of the soft approximation for the equality constraints. It should be a reasonably large value. In our experiments, we set ρ = 100. We did parameter selection for the regularization parameter γ from the set 2{ 9, 8, ,8,9} by using two-fold cross-validation on the labeled training data. |