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..
Correlated Variational Auto-Encoders
Authors: Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on matching and link prediction on public benchmark rating datasets and spectral clustering on a synthetic dataset show the effectiveness of the proposed method over baseline algorithms. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Columbia University, New York, New York, USA 2Netflix Inc., Los Gatos, California, USA 3Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas, Richardson, Texas, USA. |
| Pseudocode | Yes | Algorithm 1 Computing all weights w MAS G,e |
| Open Source Code | Yes | Code is available at https://github.com/datang1992/CorrelatedVAEs. |
| Open Datasets | Yes | We use the Movie Lens 20M dataset (Harper & Konstan, 2016). (...) In this experiment, we use the Epinions dataset (Massa & Avesani, 2007) |
| Dataset Splits | Yes | For all the experiments, we did a stochastic train/test split over users with a 90/10 ratio. (...) To split the train/test dataset for the link prediction task, for each vertex vi V , we hold out max 1, 1 20 degree(vi) edges on vi as the testing edge set Etest, and put all edges that are not selected into the training edge set Etrain. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It does not mention any specific hardware used for training or evaluation. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | For CVAEcorr, we set the negative sampling regularization parameter γ = 1. (...) Here we apply negative sampling regularization parameter γ = 0.1 for CVAEcorr. (...) Here we apply a large regularization value of γ = 1000 for the CVAEcorr |