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The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade.
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2003
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C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning . MIT Press, 2006
2006
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J. M. Wang, D. J. Fleet, and A. Hertzmann, “Gaussian process dynamical models for human motion,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 30, no. 2, pp. 283–298, 2007
2007
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M. Titsias, “Variational learning of inducing variables in sparse Gaussian processes,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS) , Clearwater, FL, United states, Apr. 2009, pp. 567–574
2009
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M. C. Jones and A. Pewsey, “Sinh-arcsinh distributions,” Biometrika , vol. 96, no. 4, pp. 761–780, 2009
2009
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R. Turner, M. Deisenroth, and C. Rasmussen, “State-space inference and learning with Gaussian processes,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS) , Sardinia, Italy, May 2010, pp. 868–875
2010
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A. G. Wilson and Z. Ghahramani, “Copula processes,” Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 23, 2010
2010
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F. L. Wauthier and M. Jordan, “Heavy-tailed process priors for selective shrinkage,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Vancouver, BC, Canada, Dec. 2010
2010
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J. Ko and D. Fox, “Learning GP-BayesFilters via Gaussian process latent variable models,” Auton. Robots , vol. 30, no. 1, pp. 3–23, Oct. 2011
2011
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M. P. Deisenroth, R. D. Turner, M. F. Huber, U. D. Hanebeck, and C. E. Rasmussen, “Robust filtering and smoothing with Gaussian processes,” IEEE Trans. Autom. Control , vol. 57, no. 7, pp. 1865–1871, 2011
2011
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T. Tao, An introduction to measure theory . American Mathematical Society Providence, RI, 2011, vol. 126
2011
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S. Särkkä, Bayesian filtering and smoothing . Cambridge University Press, 2013, no. 3
2013
Earlier work this paper cites.
M. P. Deisenroth, D. Fox, and C. E. Rasmussen, “Gaussian processes for data-efficient learning in robotics and control,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 37, no. 2, pp. 408–423, 2013
2013
Earlier work this paper cites.
R. Frigola, F. Lindsten, T. B. Schön, and C. E. Rasmussen, “Bayesian inference and learning in Gaussian process state-space models with particle MCMC,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Lake Tahoe, NV, United states, Dec. 2013, pp. 3156–3164
2013
Earlier work this paper cites.
A. Damianou and N. D. Lawrence, “Deep Gaussian processes,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS) , 2013, pp. 207–215
2013
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A. Wilson and R. Adams, “Gaussian process kernels for pattern discovery and extrapolation,” in Proc. Int. Conf. Mach. Learn. (ICML) , Atlanta, GA, United states, Jun. 2013, pp. 1067–1075
2013
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J. Hensman, N. Fusi, and N. D. Lawrence, “Gaussian processes for big data,” in Proc. Conf. Uncertain. Artif. Intell. (UAI) , Bellevue, WA, United states, Jul. 2013, pp. 282–290
2013
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R. Frigola, Y. Chen, and C. E. Rasmussen, “Variational Gaussian process state-space models,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Montreal, QC, Canada, Dec. 2014, pp. 3680–3688
2014
Earlier work this paper cites.
A. J. McHutchon, “Nonlinear modelling and control using Gaussian processes,” Ph.D. dissertation, University of Cambridge, 2014
2014
Earlier work this paper cites.
R. Frigola, “Bayesian time series learning with Gaussian processes,” Ph.D. dissertation, University of Cambridge, 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR) , San Diego, CA, United states, May 2015
2015
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A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing, “Deep kernel learning,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS) , Cadiz, Spain, May 2016, pp. 370–378
2016
Cited alongside, same era.
M. Karl, M. Soelch, J. Bayer, and P. Van der Smagt, “Deep variational Bayes filters: Unsupervised learning of state space models from raw data,” in Proc. Int. Conf. Learn. Represent. (ICLR) , Toulon, France, Apr. 2017
2017
Cited alongside, same era.
R. Krishnan, U. Shalit, and D. Sontag, “Structured inference networks for nonlinear state space models,” in Proc. AAAI Conf. Artif. Intell. (AAAI) , San Francisco, CA, United states, Feb. 2017, pp. 2101–2109
2017
Cited alongside, same era.
S. Eleftheriadis, T. Nicholson, M. P. Deisenroth, and J. Hensman, “Identification of Gaussian process state space models,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Long Beach, CA, United states, Dec. 2017, pp. 5309–5319
2017
Cited alongside, same era.
Y. Dai, T. Zhang, Z. Lin, F. Yin, S. Theodoridis, and S. Cui, “An interpretable and sample efficient deep kernel for Gaussian process,” in Proc. Conf. Uncertain. Artif. Intell. (UAI) , Virtual, Online, Aug. 2020, pp. 759–768
2020
Later among the works it cites.
I. Kobyzev, S. J. Prince, and M. A. Brubaker, “Normalizing flows: An introduction and review of current methods,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 11, pp. 3964–3979, 2020
2020
Later among the works it cites.
D. Gedon, N. Wahlström, T. B. Schön, and L. Ljung, “Deep state space models for nonlinear system identification,” IFAC-PapersOnLine , vol. 54, no. 7, pp. 481–486, 2021
2021
Later among the works it cites.
A. Kullberg, I. Skog, and G. Hendeby, “Online joint state inference and learning of partially unknown state-space models,” IEEE Trans. Signal Process. , vol. 69, pp. 4149–4161, 2021
2021
Later among the works it cites.
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I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “ β \beta -VAE: Learning basic visual concepts with a constrained variational framework,” in Proc. Int. Conf. Learn. Represent. (ICLR) , Toulon, France, Apr. 2017
2017
Cited alongside, same era.
L. Dinh and S. Bengio, “Density estimation using Real NVP,” in Proc. Int. Conf. Learn. Represent. (ICLR) , Toulon, France, Apr. 2017
2017
Cited alongside, same era.
A. Doerr, C. Daniel, M. Schiegg, N.-T. Duy, S. Schaal, M. Toussaint, and T. Sebastian, “Probabilistic recurrent state-space models,” in Proc. Int. Conf. Mach. Learn. (ICML) , Stockholm, Sweden, Jul. 2018, pp. 1280–1289
2018
Cited alongside, same era.
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Montreal, QC, Canada, Dec. 2018, pp. 6572–6583
2018
Cited alongside, same era.
A. Alemi, B. Poole, I. Fischer, J. Dillon, R. A. Saurous, and K. Murphy, “Fixing a broken ELBO,” in Proc. Int. Conf. Mach. Learn. (ICML) , Stockholm, Sweden, Jul. 2018, pp. 159–168
2018
Cited alongside, same era.
A. M. Alaa and M. van der Schaar, “Attentive state-space modeling of disease progression,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , Vancouver, BC, Canada, Dec. 2019, pp. 11 338–11 348
2019
Cited alongside, same era.
Y. Zhao, C. Fritsche, G. Hendeby, F. Yin, T. Chen, and F. Gunnarsson, “Cramér–Rao bounds for filtering based on Gaussian process state-space models,” IEEE Trans. Signal Process. , vol. 67, no. 23, pp. 5936–5951, 2019
2019
Cited alongside, same era.
A. D. Ialongo, M. van der Wilk, J. Hensman, and C. E. Rasmussen, “Overcoming mean-field approximations in recurrent Gaussian process models,” in Proc. Int. Conf. Mach. Learn. (ICML) , Long Beach, CA, United states, Jun. 2019, pp. 2931–2940
2019
Cited alongside, same era.
Y. Liu and P. M. Djurić, “Gaussian process state-space models with time-varying parameters and inducing points,” in Proc. European Signal Proces. Conf. (EUSIPCO) , Amsterdam, Netherlands, Jan. 2021, pp. 1462–1466
2021
Later among the works it cites.
J. Maroñas, O. Hamelijnck, J. Knoblauch, and T. Damoulas, “Transforming Gaussian processes with normalizing flows,” in Proc. Int. Conf. Artif. Intell. Stat. (AISTATS) , Virtual, Online, Apr. 2021, pp. 1081–1089
2021
Later among the works it cites.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” J. Mach. Learn. Res. , vol. 22, no. 57, pp. 1–64, Mar. 2021
2021
Later among the works it cites.
S. Bond-Taylor, A. Leach, Y. Long, and C. G. Willcocks, “Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 11, pp. 7327–7347, Sep. 2021
2021
Later among the works it cites.
J. Courts, A. G. Wills, and T. B. Schön, “Gaussian variational state estimation for nonlinear state-space models,” IEEE Trans. Signal Process. , vol. 69, pp. 5979–5993, Oct. 2021
2021
Later among the works it cites.
G. Revach, N. Shlezinger, X. Ni, A. L. Escoriza, R. J. Van Sloun, and Y. C. Eldar, “KalmanNet: Neural network aided Kalman filtering for partially known dynamics,” IEEE Trans. Signal Process. , vol. 70, pp. 1532–1547, Mar. 2022
2022
Later among the works it cites.
J. Lindinger, B. Rakitsch, and C. Lippert, “Laplace approximated Gaussian process state-space models,” in Proc. Conf. Uncertain. Artif. Intell. (UAI) , Eindhoven, Netherlands, Aug. 2022
2022
Later among the works it cites.
Y. Liu, M. Ajirak, and P. M. Djurić, “Inference with deep Gaussian process state space models,” in Proc. European Signal Proces. Conf. (EUSIPCO) , Belgrade, Serbia, Oct. 2022, pp. 792–796
2022
Later among the works it cites.
Y. Zhao, J. Nassar, I. Jordan, M. Bugallo, and I. M. Park, “Streaming variational monte carlo,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 1, pp. 1150–1161, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
R. C. Suwandi, Z. Lin, Y. Sun, Z. Wang, L. Cheng, and F. Yin, “Gaussian process regression with grid spectral mixture kernel: Distributed learning for multidimensional data,” in Proc. Int. Conf. Inf. Fusion (FUSION) , Linkoping, Sweden, July 2022, pp. 1–8
2022
Later among the works it cites.
L. Cheng, F. Yin, S. Theodoridis, S. Chatzis, and T.-H. Chang, “Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling,” IEEE Signal Process. Mag. , vol. 39, no. 6, pp. 18–52, Nov. 2022
2022
Later among the works it cites.
J. Knoblauch, J. Jewson, and T. Damoulas, “An optimization-centric view on bayes’ rule: Reviewing and generalizing variational inference,” J. Mach. Learn. Res. , vol. 23, no. 132, pp. 1–109, 2022
2022
Later among the works it cites.
Z. Lin, L. Cheng, F. Yin, L. Xu, and S. Cui, “Output-dependent Gaussian process state-space model,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , Rhodes, Greek, 2023, pp. 1–5
2023
Closest in time.
M. Dowling, Y. Zhao, and I. M. Park, “Real-time variational method for learning neural trajectory and its dynamics,” in Proc. Int. Conf. Learn. Represent. (ICLR) , 2023
2023
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Z. Chen, J. Fan, and K. Wang, “Multivariate Gaussian processes: definitions, examples and applications,” METRON , pp. 1–11, 2023
2023
Closest in time.