Abstract
| Language | English |
|---|---|
| Title of host publication | Proceedings of the 36th International Conference on Machine Learning |
| Editors | Kamalika Chaudhuri, Ruslan Salakhutdinov |
| Place of Publication | Long Beach, California, USA |
| Publisher | PMLR |
| Pages | 3682-3691 |
| Number of pages | 10 |
| Volume | 97 |
| Status | Published - 9 Jun 2019 |
| Event | Thirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, USA United States Duration: 9 Jun 2019 → 15 Jun 2019 Conference number: 36 https://icml.cc/ |
Publication series
| Name | Proceedings of Machine Learning Research |
|---|---|
| Publisher | PMLR |
| ISSN (Electronic) | 2368-5417 |
Conference
| Conference | Thirty-sixth International Conference on Machine Learning |
|---|---|
| Abbreviated title | ICML |
| Country | USA United States |
| City | Long Beach |
| Period | 9/06/19 → 15/06/19 |
| Internet address |
Cite this
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. / Lawrence, Andrew; Ek, Carl Henrik; Campbell, Neill.
Proceedings of the 36th International Conference on Machine Learning. ed. / Kamalika Chaudhuri; Ruslan Salakhutdinov. Vol. 97 Long Beach, California, USA : PMLR, 2019. p. 3682-3691 (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
AU - Lawrence, Andrew
AU - Ek, Carl Henrik
AU - Campbell, Neill
PY - 2019/6/9
Y1 - 2019/6/9
N2 - We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of the Dirichlet process as a specific structural prior allows our model to circumvent issues associated with previous Gaussian process latent variable models. Inference is performed by deriving an efficient variational bound on the marginal log-likelihood of the model. We demonstrate the efficacy of our approach via analysis of discovered structure and superior quantitative performance on missing data imputation.
AB - We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of the Dirichlet process as a specific structural prior allows our model to circumvent issues associated with previous Gaussian process latent variable models. Inference is performed by deriving an efficient variational bound on the marginal log-likelihood of the model. We demonstrate the efficacy of our approach via analysis of discovered structure and superior quantitative performance on missing data imputation.
UR - http://proceedings.mlr.press/v97/lawrence19a.html
UR - http://proceedings.mlr.press/v97/lawrence19a/lawrence19a-supp.pdf
M3 - Conference contribution
VL - 97
T3 - Proceedings of Machine Learning Research
SP - 3682
EP - 3691
BT - Proceedings of the 36th International Conference on Machine Learning
A2 - Chaudhuri, Kamalika
A2 - Salakhutdinov, Ruslan
PB - PMLR
CY - Long Beach, California, USA
ER -