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05/30/2019
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Despite the recent progress in hyperparameter optimization (HPO), availa...
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04/24/2019
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
Continual learning aims to enable machine learning models to learn a gen...
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03/18/2019
Deep Gaussian Processes for Multi-fidelity Modeling
Multi-fidelity methods are prominently used when cheaply-obtained, but p...
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03/12/2019
Continual Learning in Practice
This paper describes a reference architecture for self-maintaining syste...
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01/03/2018
Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (in-GPs) for interpol...
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10/25/2016
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
We propose a parallelizable sparse inverse formulation Gaussian process ...
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10/17/2016
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
Quantitative modeling of post-transcriptional regulation process is a ch...
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06/30/2016
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when gi...
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11/19/2015
Variational Auto-encoded Deep Gaussian Processes
We develop a scalable deep non-parametric generative model by augmenting...
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05/10/2015
Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GP-LVM) is a popular approac...
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10/18/2014
Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models wi...
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06/18/2012
Manifold Relevance Determination
In this paper we present a fully Bayesian latent variable model which ex...
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08/16/2011