On Estimation in Latent Variable Models

Guanhua Fang, Ping Li
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:3100-3110, 2021.

Abstract

Latent variable models have been playing a central role in statistics, econometrics, machine learning with applications to repeated observation study, panel data inference, user behavior analysis, etc. In many modern applications, the inference based on latent variable models involves one or several of the following features: the presence of complex latent structure, the observed and latent variables being continuous or discrete, constraints on parameters, and data size being large. Therefore, solving an estimation problem for general latent variable models is highly non-trivial. In this paper, we consider a gradient based method via using variance reduction technique to accelerate estimation procedure. Theoretically, we show the convergence results for the proposed method under general and mild model assumptions. The algorithm has better computational complexity compared with the classical gradient methods and maintains nice statistical properties. Various numerical results corroborate our theory.

Cite this Paper


BibTeX
@InProceedings{pmlr-v139-fang21a, title = {On Estimation in Latent Variable Models}, author = {Fang, Guanhua and Li, Ping}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {3100--3110}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/fang21a/fang21a.pdf}, url = {https://proceedings.mlr.press/v139/fang21a.html}, abstract = {Latent variable models have been playing a central role in statistics, econometrics, machine learning with applications to repeated observation study, panel data inference, user behavior analysis, etc. In many modern applications, the inference based on latent variable models involves one or several of the following features: the presence of complex latent structure, the observed and latent variables being continuous or discrete, constraints on parameters, and data size being large. Therefore, solving an estimation problem for general latent variable models is highly non-trivial. In this paper, we consider a gradient based method via using variance reduction technique to accelerate estimation procedure. Theoretically, we show the convergence results for the proposed method under general and mild model assumptions. The algorithm has better computational complexity compared with the classical gradient methods and maintains nice statistical properties. Various numerical results corroborate our theory.} }
Endnote
%0 Conference Paper %T On Estimation in Latent Variable Models %A Guanhua Fang %A Ping Li %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-fang21a %I PMLR %P 3100--3110 %U https://proceedings.mlr.press/v139/fang21a.html %V 139 %X Latent variable models have been playing a central role in statistics, econometrics, machine learning with applications to repeated observation study, panel data inference, user behavior analysis, etc. In many modern applications, the inference based on latent variable models involves one or several of the following features: the presence of complex latent structure, the observed and latent variables being continuous or discrete, constraints on parameters, and data size being large. Therefore, solving an estimation problem for general latent variable models is highly non-trivial. In this paper, we consider a gradient based method via using variance reduction technique to accelerate estimation procedure. Theoretically, we show the convergence results for the proposed method under general and mild model assumptions. The algorithm has better computational complexity compared with the classical gradient methods and maintains nice statistical properties. Various numerical results corroborate our theory.
APA
Fang, G. & Li, P.. (2021). On Estimation in Latent Variable Models. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:3100-3110 Available from https://proceedings.mlr.press/v139/fang21a.html.

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