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Bayesian nonparametric shared multi-sequence time series segmentation
In this paper, we introduce a method for segmenting time series data usi...
01/27/2020 ∙ by Olga Mikheeva, et al. ∙
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Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
09/17/2019 ∙ by Ivan Ustyuzhaninov, et al. ∙
13
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Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
06/26/2019 ∙ by Erik Bodin, et al. ∙
2
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Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
05/30/2019 ∙ by Ivan Ustyuzhaninov, et al. ∙
0
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Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of high-dime...
11/26/2018 ∙ by Ieva Kazlauskaite, et al. ∙
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Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high-...
03/07/2018 ∙ by Ieva Kazlauskaite, et al. ∙
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