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Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds
We propose a general scheme for solving convex and non-convex optimizati...
10/18/2020 ∙ by Lizhen Lin, et al. ∙
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Latent variable modeling with random features
Gaussian process-based latent variable models are flexible and theoretic...
06/19/2020 ∙ by Gregory W. Gundersen, et al. ∙
18
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Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
01/15/2020 ∙ by Avinava Dubey, et al. ∙
33
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Probabilistic Time of Arrival Localization
In this paper, we take a new approach for time of arrival geo-localizati...
10/15/2019 ∙ by Fernando Perez-Cruz, et al. ∙
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Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimatin...
05/24/2019 ∙ by Michael Minyi Zhang, et al. ∙
16
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A New Class of Time Dependent Latent Factor Models with Applications
In many applications, observed data are influenced by some combination o...
04/18/2019 ∙ by Sinead A. Williamson, et al. ∙
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Communication Efficient Parallel Algorithms for Optimization on Manifolds
The last decade has witnessed an explosion in the development of models,...
10/26/2018 ∙ by Bayan Saparbayeva, et al. ∙
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Accelerated Inference for Latent Variable Models
Inference of latent feature models in the Bayesian nonparametric setting...
05/19/2017 ∙ by Michael Minyi Zhang, et al. ∙
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Robust and Parallel Bayesian Model Selection
Effective and accurate model selection is an important problem in modern...
10/19/2016 ∙ by Michael Minyi Zhang, et al. ∙
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