Variational Learning Approach for Sparse Pseudo-input Gaussian Process Policy Search

Hikaru SASAKI; Yuto OZAWA; Takamitsu MATSUBARA

doi:10.1299/jsmermd.2018.1a1-c16

Variational Learning Approach for Sparse Pseudo-input Gaussian Process Policy Search

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2018.1a1-c16 ◽

2018 ◽

Vol 2018 (0) ◽

pp. 1A1-C16

Author(s):

Hikaru SASAKI ◽

Yuto OZAWA ◽

Takamitsu MATSUBARA

Keyword(s):

Gaussian Process ◽

Learning Approach ◽

Policy Search ◽

Variational Learning

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Statistics manifold learning approach and its application to non-Gaussian process monitoring

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9189523 ◽

2020 ◽

Author(s):

Ping Cui ◽

Xuhong Wang ◽

Yupu Yang

Keyword(s):

Gaussian Process ◽

Process Monitoring ◽

Manifold Learning ◽

Learning Approach ◽

Non Gaussian

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A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2021.3112138 ◽

2021 ◽

pp. 1-1

Author(s):

Paolo Manfredi ◽

Riccardo Trinchero

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Uncertainty Quantification ◽

Gaussian Process Regression ◽

Learning Approach ◽

Electronic Circuits ◽

Machine Learning Approach ◽

Probabilistic Machine Learning

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Microscopic Modeling of Cyclists Interactions with Pedestrians in Shared Spaces: A Gaussian Process Inverse Reinforcement Learning Approach

Transportmetrica A Transport Science ◽

10.1080/23249935.2021.1898487 ◽

2021 ◽

pp. 1-28

Author(s):

Rushdi Alsaleh ◽

Tarek Sayed

Keyword(s):

Reinforcement Learning ◽

Gaussian Process ◽

Learning Approach ◽

Inverse Reinforcement Learning ◽

Shared Spaces ◽

Microscopic Modeling

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Multimodal Policy Search using Overlapping Mixtures of Sparse Gaussian Process Prior

2019 International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2019.8794131 ◽

2019 ◽

Author(s):

Hikaru Sasaki ◽

Takamitsu Matsubara

Keyword(s):

Gaussian Process ◽

Policy Search

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Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.195 ◽

2020 ◽

Vol 53 (2) ◽

pp. 388-393 ◽

Cited By ~ 1

Author(s):

Yvonne R. Stürz ◽

Mohammad Khosravi ◽

Roy S. Smith

Keyword(s):

Parameter Identification ◽

Gaussian Process ◽

Digital Fabrication ◽

Learning Approach ◽

Process Learning

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Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340709 ◽

2020 ◽

Author(s):

Juan Antonio Delgado-Guerrero ◽

Adria Colome ◽

Carme Torras

Keyword(s):

Gaussian Process ◽

Latent Variable ◽

Latent Variable Models ◽

Robot Motion ◽

Micro Data ◽

Policy Search ◽

Motion Learning

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Fast Variational Learning in State-Space Gaussian Process Models

2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) ◽

10.1109/mlsp49062.2020.9231560 ◽

2020 ◽

Author(s):

Paul E. Chang ◽

William J. Wilkinson ◽

Mohammad Emtiyaz Khan ◽

Arno Solin

Keyword(s):

Gaussian Process ◽

State Space ◽

Process Models ◽

Gaussian Process Models ◽

Variational Learning

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A structured variational learning approach for switching latent factor models

AStA Advances in Statistical Analysis ◽

10.1007/s10182-007-0031-4 ◽

2007 ◽

Vol 91 (3) ◽

pp. 245-268 ◽

Cited By ~ 3

Author(s):

Mohamed Saidane ◽

Christian Lavergne

Keyword(s):

Factor Models ◽

Learning Approach ◽

Latent Factor ◽

Latent Factor Models ◽

Variational Learning

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Gaussian Process Self-triggered Policy Search

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2021.1p1-i17 ◽

2021 ◽

Vol 2021 (0) ◽

pp. 1P1-I17

Author(s):

Hikaru SASAKI ◽

Takamitsu MATSUBARA

Keyword(s):

Gaussian Process ◽

Policy Search

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Variational policy search using sparse Gaussian process priors for learning multimodal optimal actions

Neural Networks ◽

10.1016/j.neunet.2021.06.010 ◽

2021 ◽

Author(s):

Hikaru Sasaki ◽

Takamitsu Matsubara

Keyword(s):

Gaussian Process ◽

Policy Search

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