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This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises.
L. Csató and M. Opper, “Sparse online Gaussian processes,” Neural Comput. , vol. 14, pp. 641–669, 2002
2002
Earlier work this paper cites.
M. Seeger, C. Williams, and N. D. Lawrence, “Fast forward selection to speed up sparse Gaussian process regression,” in Proc. AISTATS , 2003
2003
Earlier work this paper cites.
J. Quiñonero-Candela and C. E. Rasmussen, “A unifying view of sparse approximate Gaussian process regression,” JMLR , vol. 6, pp. 1939–1959, 2005
2005
Earlier work this paper cites.
E. L. Snelson and Z. Gharahmani, “Sparse Gaussian processes using pseudo-inputs,” in Proc. NIPS , 2005, pp. 1257–1264
2005
Earlier work this paper cites.
E. L. Snelson and Z. Ghahramani, “Local and global sparse Gaussian process approximations,” in Proc. AISTATS , 2007
2007
Earlier work this paper cites.
M. J. Wainwright and M. I. Jordan, “Graphical models, exponential families, and variational inference,” Foundations and Trends ®
2008
Earlier work this paper cites.
K. H. Low, J. M. Dolan, and P. Khosla, “Adaptive multi-robot wide-area exploration and mapping,” in Proc. AAMAS , 2008, pp. 23–30
2008
Earlier work this paper cites.
M. K. Titsias, “Variational model selection for sparse Gaussian process regression,” School of Computer Science, University of Manchester, Tech. Rep., 2009
2009
Earlier work this paper cites.
——, “Variational learning of inducing variables in sparse Gaussian processes,” in Proc. AISTATS , 2009, pp. 567–574
2009
Earlier work this paper cites.
——, “Information-theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing,” in Proc. ICAPS , 2009, pp. 233–240
2009
Earlier work this paper cites.
M. Lázaro-Gredilla, J. Quiñonero-Candela, C. E. Rasmussen, and A. R. Figueiras-Vidal, “Sparse spectrum Gaussian process regression,” JMLR , vol. 11, pp. 1865–1881, 2010
2010
Earlier work this paper cites.
——, “Active Markov information-theoretic path planning for robotic environmental sensing,” in Proc. AAMAS , 2011, pp. 753–760
2011
Earlier work this paper cites.
J. Chen, K. H. Low, C. K.-Y. Tan, A. Oran, P. Jaillet, J. M. Dolan, and G. S. Sukhatme, “Decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena,” in Proc. UAI , 2012, pp. 163–173
2012
Earlier work this paper cites.
K. B. Petersen and M. S. Pedersen, “The matrix cookbook,” 2012
2012
Earlier work this paper cites.
J. Chen, N. Cao, K. H. Low, R. Ouyang, C. K.-Y. Tan, and P. Jaillet, “Parallel Gaussian process regression with low-rank covariance matrix approximations,” in Proc. UAI , 2013, pp. 152–161
2013
Cited alongside, same era.
J. Hensman, N. Fusi, and N. Lawrence, “Gaussian processes for big data,” in Proc. UAI , 2013, pp. 282–290
2013
Cited alongside, same era.
M. K. Titsias and M. Lázaro-Gredilla, “Variational inference for Mahalanobis distance metrics in Gaussian process regression,” in Proc. NIPS , 2013, pp. 279–287
2013
Cited alongside, same era.
N. Cao, K. H. Low, and J. M. Dolan, “Multi-robot informative path planning for active sensing of environmental phenomena: A tale of two algorithms,” in Proc. AAMAS , 2013, pp. 7–14
2013
Cited alongside, same era.
J. Chen, K. H. Low, and C. K. Y. Tan, “Gaussian process-based decentralized data fusion and active sensing for mobility-on-demand system,” in Proc. RSS , 2013
Y. Gal and R. Turner, “Improving the Gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs,” in Proc. ICML , 2015
2015
Later among the works it cites.
M. P. Deisenroth and J. W. Ng, “Distributed Gaussian processes,” in Proc. ICML , 2015, pp. 1481–1490
2015
Later among the works it cites.
J. Chen, K. H. Low, P. Jaillet, and Y. Yao, “Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems,” IEEE Transactions on Automation Science and Engineering , vol. 12, no. 3, pp. 901–921, 2015
2015
Later among the works it cites.
C.-A. Cheng and B. Boots, “Incremental variational sparse Gaussian process regression,” in Proc. NIPS , 2016, pp. 4410–4418
2016
Later among the works it cites.
——, “A distributed variational inference framework for unifying parallel sparse Gaussian process regression models,” in Proc. ICML , 2016
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2013
Cited alongside, same era.
N. Xu, K. H. Low, J. Chen, K. K. Lim, and E. B. Özgül, “GP-Localize: Persistent mobile robot localization using online sparse Gaussian process observation model,” in Proc. AAAI , 2014, pp. 2585–2592
2014
Cited alongside, same era.
Y. Gal, M. van der Wilk, and C. E. Rasmussen, “Distributed variational inference in sparse Gaussian process regression and latent variable models,” in Proc. NIPS , 2014, pp. 3257–3265
2014
Cited alongside, same era.
T. N. Hoang, K. H. Low, P. Jaillet, and M. Kankanhalli, “Nonmyopic ϵ \epsilon -Bayes-optimal active learning of Gaussian processes,” in Proc. ICML , 2014, pp. 739–747
2014
Cited alongside, same era.
R. Ouyang, K. H. Low, J. Chen, and P. Jaillet, “Multi-robot active sensing of non-stationary Gaussian process-based environmental phenomena,” in Proc. AAMAS , 2014, pp. 573–580
2014
Cited alongside, same era.
J. M. Hernández-Lobato, M. W. Hoffman, and Z. Ghahramani, “Predictive entropy search for efficient global optimization of black-box functions,” in Proc. NIPS , 2014, pp. 918–926
2014
Cited alongside, same era.
Q. M. Hoang, T. N. Hoang, and K. H. Low, “A generalized stochastic variational Bayesian hyperparameter learning framework for sparse spectrum Gaussian process regression,” in Proc. AAAI , 2017, pp. 2007–2014
2014
Cited alongside, same era.
K. H. Low, J. Yu, J. Chen, and P. Jaillet, “Parallel Gaussian process regression for big data: Low-rank representation meets Markov approximation,” in Proc. AAAI , 2015, pp. 2821–2827
2015
Cited alongside, same era.
2016
Later among the works it cites.
Y. Zhang, T. N. Hoang, K. H. Low, and M. Kankanhalli, “Near-optimal active learning of multi-output Gaussian processes,” in Proc. AAAI , 2016, pp. 2351–2357
2016
Later among the works it cites.
C. K. Ling, K. H. Low, and P. Jaillet, “Gaussian process planning with Lipschitz continuous reward functions: Towards unifying Bayesian optimization, active learning, and beyond,” in Proc. AAAI , 2016, pp. 1860–1866
2016
Later among the works it cites.
M. Bauer, M. van der Wilk, and C. E. Rasmussen, “Understanding probabilistic sparse Gaussian process approximations,” in Proc. NIPS , 2016, pp. 1533–1541
2016
Later among the works it cites.
T. D. Bui, C. Nguyen, and R. E. Turner, “Streaming sparse Gaussian process approximations,” in Proc. NIPS , 2017, pp. 3301–3309
2017
Closest in time.
E. Daxberger and K. H. Low, “Distributed batch Gaussian process optimization,” in Proc. ICML , 2017, pp. 951–960
2017
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2017
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T. N. Hoang, Q. M. Hoang, and K. H. Low, “Decentralized high-dimensional Bayesian optimization with factor graphs,” in Proc. AAAI , 2018, pp. 3231–3238
2018
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R. Ouyang and K. H. Low, “Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception,” in Proc. AAAI , 2018, pp. 3876–3883
2018
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T. N. Hoang, Q. M. Hoang, K. H. Low, and J. P. How, “Collective online learning of Gaussian processes in massive multi-agent systems,” in Proc. AAAI , 2019
2019
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