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Standard kernels such as Mat\'ern or RBF kernels only encode simple monotonic dependencies within the input space.
Silverman R (1957) Locally stationary random processes. Information Theory, IRE Transactions on 3:182–187
1957
Earlier work this paper cites.
Rioul O, Martin V (1991) Wavelets and signal processing. IEEE signal processing magazine 8:14–38
1991
Earlier work this paper cites.
Sampson P, Guttorp P (1992) Nonparametric estimation of nonstationary spatial covariance structure. Journal of the American Statistical Association 87
1992
Earlier work this paper cites.
Gibbs M (1997) Bayesian Gaussian processes for regression and classification. PhD thesis, University of Cambridge
1997
Earlier work this paper cites.
Huang N, Zheng S, Long S, Wu M, Shih H, Zheng Q, Yen NQ, Tung C, Liu H (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 454:903–995
1998
Earlier work this paper cites.
Higdon D, Swall J, Kern J (1999) Non-stationary spatial modeling. Bayesian statistics 6:761–768
1999
Earlier work this paper cites.
Genton M (2001) Classes of kernels for machine learning: A statistics perspective. Journal of Machine Learning Research 2:299–312
2001
Earlier work this paper cites.
Lean J (2004) Solar irradiance reconstruction. IGBP PAGES/World Data Center for Paleoclimatology, Data Contribution Series # 2004-035, NOAA/NGDC Paleoclimatology Program, Boulder CO, USA
2004
Earlier work this paper cites.
Paciorek C, Schervish M (2004) Nonstationary covariance functions for Gaussian process regression. In: NIPS, pp 273–280
2004
Earlier work this paper cites.
Paciorek C, Schervish M (2006) Spatial modelling using a new class of nonstationary covariance functions. Environmetrics 17(5):483–506
2006
Earlier work this paper cites.
Rasmussen CE, Williams C (2006) Gaussian processes for machine learning. MIT Press
2006
Earlier work this paper cites.
Cunningham JP, Shenoy KV, Sahani M (2008) Fast Gaussian process methods for point process intensity estimation. In: ICML, pp 192–199
2008
Earlier work this paper cites.
Gramacy R, Lee H (2008) Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association 103:1119–1130
2008
Earlier work this paper cites.
Huang N (2008) A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Reviews of Geophysics 46
2008
Earlier work this paper cites.
Rahimi A, Recht B (2008) Random features for large-scale kernel machines. In: NIPS, pp 1177–1184
2008
Cited alongside, same era.
Robinson J, Hartemink A (2009) Non-stationary dynamic Bayesian networks. In: NIPS, pp 1369–1376
2009
Cited alongside, same era.
Hartikainen J, Särkkä S (2010) Kalman filtering and smoothing solutions to temporal gaussian process regression models. In: Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on, IEEE, pp 379–384
2010
Cited alongside, same era.
Lázaro-Gredilla M, Quiñonero-Candela J, Rasmussen CE, Figueiras-Vidal AR (2010) Sparse spectrum Gaussian process regression. Journal of Machine Learning Research 11:1865–1881
2010
Cited alongside, same era.
Saatçi Y (2011) Scalable inference for structured Gaussian process models. PhD thesis, University of Cambridge
2011
Wilson A, Nickisch H (2015) Kernel interpolation for scalable structured Gaussian processes (KISS-GP). In: International Conference on Machine Learning, pp 1775–1784
2015
Later among the works it cites.
Yang Z, Smola A, Song L, Wilson A (2015) A la carte: Learning fast kernels. In: AISTATS
2015
Later among the works it cites.
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press
2016
Later among the works it cites.
Heinonen M, Mannerström H, Rousu J, Kaski S, Lähdesmäki H (2016) Non-stationary Gaussian process regression with Hamiltonian Monte Carlo. In: AISTATS, vol 51, pp 732–740
2016
Later among the works it cites.
Sinha A, Duchi J (2016) Learning lernels with random features. In: NIPS
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:13126114
2013
Cited alongside, same era.
Wilson AG, Adams R (2013) Gaussian process kernels for pattern discovery and extrapolation. In: ICML
2013
Cited alongside, same era.
Snoek J, Swersky K, Zemel R, Adams R (2014) Input warping for Bayesian optimization of non-stationary functions. In: ICML, vol 32, pp 1674–1682
2014
Cited alongside, same era.
Tolvanen V, Jylänki P, Vehtari A (2014) Expectation propagation for nonstationary heteroscedastic Gaussian process regression. In: Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, IEEE, pp 1–6
2014
Cited alongside, same era.
Wilson AG (2014) Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes. PhD thesis, University of Cambridge
2014
Cited alongside, same era.
Wilson AG, Gilboa E, Nehorai A, Cunningham JP (2014) Fast kernel learning for multidimensional pattern extrapolation. In: NIPS, pp 3626–3634
2014
Cited alongside, same era.
Gal Y, Turner R (2015) Improving the gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs. In: International Conference on Machine Learning, pp 655–664
2015
Cited alongside, same era.
Wilson AG, Hu Z, Salakhutdinov R, Xing EP (2016) Deep kernel learning. In: Artificial Intelligence and Statistics, pp 370–378
2016
Later among the works it cites.
Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks. In: NIPS, pp 971–980
2017
Later among the works it cites.
Matthews AGdG, van der Wilk M, Nickson T, Fujii K, Boukouvalas A, León-Villagrá P, Ghahramani Z, Hensman J (2017) GPflow: A Gaussian process library using TensorFlow. Journal of Machine Learning Research 18(40):1–6, URL http://jmlr.org/papers/v18/16-537.html
2017
Later among the works it cites.
Remes S, Heinonen M, Kaski S (2017) Non-stationary spectral kernels. In: NIPS
2017
Later among the works it cites.
Hensman J, Durrande N, Solin A (2018) Variational Fourier features for Gaussian processes. Journal of Machine Learning Research 18(151):1–52
2018
Closest in time.
Nickisch H, Solin A, Grigorevskiy A (2018) State space Gaussian processes with non-Gaussian likelihood. In: ICML, pp 3789–3798
2018
Closest in time.
SILSO World Data Center (1749-2018) The international sunspot number. International Sunspot Number Monthly Bulletin and online catalogue http://www.sidc.be/silso/
2018
Closest in time.
Ton JF, Flaxman S, Sejdinovic D, Bhatt S (2018) Spatial mapping with Gaussian processes and nonstationary Fourier features. Spatial Statistics
2018
Closest in time.
Grzegorczyk M, Husmeier D, Edwards K, Ghazal P, Millar A (2008) Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics 24:2071–2078
2078
Closest in time.