Projects per year
Abstract
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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 665992
Original language | English |
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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 |
Publication 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 |
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Publisher | PMLR |
ISSN (Electronic) | 2368-5417 |
Conference
Conference | Thirty-sixth International Conference on Machine Learning |
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Abbreviated title | ICML |
Country/Territory | USA United States |
City | Long Beach |
Period | 9/06/19 → 15/06/19 |
Internet address |
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Dive into the research topics of 'DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures'. Together they form a unique fingerprint.Projects
- 1 Finished
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Fincham Haines, T. (CoI), Hall, P. (CoI), Kim, K. I. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Richardt, C. (CoI), Salo, A. (CoI), Seminati, E. (CoI), Tabor, A. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council