@inproceedings{Lu2022b,

title = {Gaussian Copula Embeddings},

author = {Chien Lu and Jaakko Peltonen},

url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/8ae260afda41b45ed77be58358a6c519-Paper-Conference.pdf},

year  = {2022},

date = {2022-11-28},

urldate = {2022-11-28},

booktitle = {36th Conference on Neural Information Processing Systems (NeurIPS 2022)},

abstract = {Learning latent vector representations via embedding models has been shown

promising in machine learning. However, most of the embedding models are still

limited to a single type of observed data. We propose a Gaussian copula embedding

model to learn latent vectorial representations of items in a heterogeneous-data

setting. The proposed model can effectively incorporate different types of observed

data and, at the same time, yield robust embeddings. We demonstrate that the

proposed model can effectively learn in many different scenarios, outperforming

competing models in modeling quality and task performance.},

keywords = {Data, Gaussian copula embedding model, Machine learning, Vectorial representations},

pubstate = {published},

tppubtype = {inproceedings}

}