DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Andrew Lawrence; Carl Henrik Ek; Neill Campbell

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Andrew Lawrence, Carl Henrik Ek, Neill Campbell

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a published conference proceeding

1 Citation (SciVal)

107 Downloads (Pure)

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

Original 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
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
Publisher	PMLR
ISSN (Electronic)	2368-5417

Conference

Conference	Thirty-sixth International Conference on Machine Learning
Abbreviated title	ICML
Country/Territory	USA United States
City	Long Beach
Period	9/06/19 → 15/06/19
Internet address	https://icml.cc/

Access to Document

paper.pdfAccepted author manuscript, 1.67 MB
supplemental.pdfAccepted author manuscript, 1.25 MB

http://proceedings.mlr.press/v97/lawrence19a/lawrence19a.pdf

Cite this

Lawrence, A., Ek, C. H., & Campbell, N. (2019). DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. In K. Chaudhuri, & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3682-3691). (Proceedings of Machine Learning Research). PMLR. http://proceedings.mlr.press/v97/lawrence19a/lawrence19a.pdf

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 › Chapter in a published conference proceeding

Lawrence, A, Ek, CH & Campbell, N 2019, DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. in K Chaudhuri & R Salakhutdinov (eds), Proceedings of the 36th International Conference on Machine Learning. vol. 97, Proceedings of Machine Learning Research, PMLR, Long Beach, California, USA, pp. 3682-3691, Thirty-sixth International Conference on Machine Learning, Long Beach, USA United States, 9/06/19. <http://proceedings.mlr.press/v97/lawrence19a/lawrence19a.pdf>

Lawrence, Andrew ; Ek, Carl Henrik ; Campbell, Neill. / DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. Proceedings of the 36th International Conference on Machine Learning. editor / Kamalika Chaudhuri ; Ruslan Salakhutdinov. Vol. 97 Long Beach, California, USA : PMLR, 2019. pp. 3682-3691 (Proceedings of Machine Learning Research).

@inproceedings{3843e10d7a2e4769ae499f10b0013cba,

title = "DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures",

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{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie grant agreement No 665992 ",

author = "Andrew Lawrence and Ek, {Carl Henrik} and Neill Campbell",

year = "2019",

month = jun,

day = "9",

language = "English",

volume = "97",

series = "Proceedings of Machine Learning Research",

publisher = "PMLR",

pages = "3682--3691",

editor = "Kamalika Chaudhuri and Ruslan Salakhutdinov",

booktitle = "Proceedings of the 36th International Conference on Machine Learning",

note = "Thirty-sixth International Conference on Machine Learning, ICML ; Conference date: 09-06-2019 Through 15-06-2019",

url = "https://icml.cc/",

}

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

N1 - Conference code: 36

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.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

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.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

UR - http://proceedings.mlr.press/v97/lawrence19a.html

UR - http://proceedings.mlr.press/v97/lawrence19a/lawrence19a-supp.pdf

M3 - Chapter in a published conference proceeding

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

T2 - Thirty-sixth International Conference on Machine Learning

Y2 - 9 June 2019 through 15 June 2019

ER -

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

Cite this

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Projects

Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)

Cite this