* [Bayesian Deep Nets and Deep Gaussian Processes](#bayesian-deep-nets-and-deep-gaussian-processes)

* [Inference for Deep Gaussian Processes](#inference-for-deep-gaussian-processes)

* [Convolutional Nets and Gaussian Processes](#convolutional-nets-and-gaussian-processes)

* [Bayesian Convolutional Nets](#bayesian-convolutional-nets)

* [Calibration of Bayesian Convolutional Nets](#calibration-of-bayesian-convolutional-nets)

* [Random Feature Expansions for Shallow and Deep Gaussian Processes](#random-feature-expansions-for-shallow-and-deep-gaussian-processes)

* [Variational Inference](#variational-inference)

* [Variational Inference for Gaussian Process Models](#variational-inference-for-gaussian-process-models)

* [Unsupervised learning with Deep Gaussian Processes](#unsupervised-learning-with-deep-gaussian-processes)

* [Multi-task Learning with Gaussian Processes](#multi-task-learning-with-gaussian-processes)

* [Bayesian Optimization](#bayesian-optimization)

* [Other GP and DGP Models](#other-gp-and-dgp-models)


### Bayesian Deep Nets and Deep Gaussian Processes
<ul>



<li><a name="Matthews18"></a>

A.&nbsp;G. de&nbsp;G.&nbsp;Matthews, J.&nbsp;Hron, M.&nbsp;Rowland, R.&nbsp;E. Turner, and Z.&nbsp;Ghahramani.
 Gaussian process behaviour in wide deep neural networks.
 In <em>International Conference on Learning Representations</em>, 2018.

</li>

<li><a name="Cutajar17"></a>

K.&nbsp;Cutajar, E.&nbsp;V. Bonilla, P.&nbsp;Michiardi, and M.&nbsp;Filippone.
 Random feature expansions for deep Gaussian processes.
 In D.&nbsp;Precup and Y.&nbsp;W. Teh, editors, <em>Proceedings of the 34th
  International Conference on Machine Learning</em>, volume&nbsp;70 of <em>Proceedings
  of Machine Learning Research</em>, pages 884--893, International Convention
  Centre, Sydney, Australia, Aug. 2017. PMLR.

</li>

<li><a name="Gal16"></a>

Y.&nbsp;Gal and Z.&nbsp;Ghahramani.
 Dropout As a Bayesian Approximation: Representing Model Uncertainty
  in Deep Learning.
 In <em>Proceedings of the 33rd International Conference on
  International Conference on Machine Learning - Volume 48</em>, ICML'16, pages
  1050--1059. JMLR.org, 2016.

</li>

<li><a name="Duvenaud14"></a>

D.&nbsp;K. Duvenaud, O.&nbsp;Rippel, R.&nbsp;P. Adams, and Z.&nbsp;Ghahramani.
 Avoiding pathologies in very deep networks.
 In <em>Proceedings of the Seventeenth International Conference on
  Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland,
  April 22-25, 2014</em>, volume&nbsp;33 of <em>JMLR Workshop and Conference
  Proceedings</em>, pages 202--210. JMLR.org, 2014.

</li>

<li><a name="Neal96"></a>

R.&nbsp;M. Neal.
 <em>Bayesian Learning for Neural Networks (Lecture Notes in
  Statistics)</em>.
 Springer, 1 edition, Aug. 1996.

</li></ul>
### Inference for Deep Gaussian Processes
<ul>



<li><a name="Salimbeni17"></a>

H.&nbsp;Salimbeni and M.&nbsp;Deisenroth.
 Doubly Stochastic Variational Inference for Deep Gaussian
  Processes.
 In I.&nbsp;Guyon, U.&nbsp;V. Luxburg, S.&nbsp;Bengio, H.&nbsp;Wallach, R.&nbsp;Fergus,
  S.&nbsp;Vishwanathan, and R.&nbsp;Garnett, editors, <em>Advances in Neural Information
  Processing Systems 30</em>, pages 4588--4599. Curran Associates, Inc., 2017.

</li>

<li><a name="Hoffman17"></a>

M.&nbsp;D. Hoffman.
 Learning deep latent Gaussian models with Markov chain Monte
  Carlo.
 In D.&nbsp;Precup and Y.&nbsp;W. Teh, editors, <em>Proceedings of the 34th
  International Conference on Machine Learning</em>, volume&nbsp;70 of <em>Proceedings
  of Machine Learning Research</em>, pages 1510--1519, International Convention
  Centre, Sydney, Australia, Aug. 2017. PMLR.

</li>

<li><a name="Havasi18"></a>

M.&nbsp;Havasi, J.&nbsp;M. Hern&aacute;ndez-Lobato, and J.&nbsp;J. Murillo-Fuentes.
 Inference in Deep Gaussian Processes using Stochastic Gradient
  Hamiltonian Monte Carlo.
 In S.&nbsp;Bengio, H.&nbsp;Wallach, H.&nbsp;Larochelle, K.&nbsp;Grauman, N.&nbsp;Cesa-Bianchi,
  and R.&nbsp;Garnett, editors, <em>Advances in Neural Information Processing
  Systems 31</em>, pages 7506--7516. Curran Associates, Inc., 2018.

</li>

<li><a name="Cutajar17"></a>

K.&nbsp;Cutajar, E.&nbsp;V. Bonilla, P.&nbsp;Michiardi, and M.&nbsp;Filippone.
 Random feature expansions for deep Gaussian processes.
 In D.&nbsp;Precup and Y.&nbsp;W. Teh, editors, <em>Proceedings of the 34th
  International Conference on Machine Learning</em>, volume&nbsp;70 of <em>Proceedings
  of Machine Learning Research</em>, pages 884--893, International Convention
  Centre, Sydney, Australia, Aug. 2017. PMLR.

</li>

<li><a name="Bui16"></a>

T.&nbsp;D. Bui, D.&nbsp;Hern&aacute;ndez-Lobato, J.&nbsp;M. Hern&aacute;ndez-Lobato, Y.&nbsp;Li, and
  R.&nbsp;E. Turner.
 Deep Gaussian Processes for Regression using Approximate Expectation
  Propagation.
 In M.-F. Balcan and K.&nbsp;Q. Weinberger, editors, <em>Proceedings of
  the 33nd International Conference on Machine Learning, ICML 2016, New York
  City, NY, USA, June 19-24, 2016</em>, volume&nbsp;48, pages 1472--1481. JMLR.org,
  2016.

</li>

<li><a name="Damianou13"></a>

A.&nbsp;C. Damianou and N.&nbsp;D. Lawrence.
 Deep Gaussian Processes.
 In <em>Proceedings of the Sixteenth International Conference on
  Artificial Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA,
  April 29 - May 1, 2013</em>, volume&nbsp;31 of <em>JMLR Proceedings</em>, pages
  207--215. JMLR.org, 2013.

</li>

<li><a name="Hensman14"></a>

J.&nbsp;Hensman and N.&nbsp;D. Lawrence.
 Nested Variational Compression in Deep Gaussian Processes, Dec.
  2014.
 arxiv:1412.1370.

</li></ul>
### Convolutional Nets and Gaussian Processes
<ul>



<li><a name="Kumar18"></a>

V.&nbsp;Kumar, V.&nbsp;Singh, P.&nbsp;K. Srijith, and A.&nbsp;Damianou.
 Deep Gaussian Processes with Convolutional Kernels, June 2018.
 arXiv:1806.01655.

</li>

<li><a name="VanDerWilk17"></a>

M.&nbsp;van&nbsp;der Wilk, C.&nbsp;E. Rasmussen, and J.&nbsp;Hensman.
 Convolutional Gaussian Processes.
 In I.&nbsp;Guyon, U.&nbsp;V. Luxburg, S.&nbsp;Bengio, H.&nbsp;Wallach, R.&nbsp;Fergus,
  S.&nbsp;Vishwanathan, and R.&nbsp;Garnett, editors, <em>Advances in Neural Information
  Processing Systems 30</em>, pages 2849--2858. Curran Associates, Inc., 2017.

</li>

<li><a name="Bradshaw17"></a>

J.&nbsp;Bradshaw, Alexander, and Z.&nbsp;Ghahramani.
 Adversarial Examples, Uncertainty, and Transfer Testing Robustness
  in Gaussian Process Hybrid Deep Networks, July 2017.
 arXiv:1707.02476.

</li>

<li><a name="Calandra16"></a>

R.&nbsp;Calandra, J.&nbsp;Peters, C.&nbsp;E. Rasmussen, and M.&nbsp;P. Deisenroth.
 Manifold Gaussian Processes for regression.
 In <em>2016 International Joint Conference on Neural Networks,
  IJCNN 2016, Vancouver, BC, Canada, July 24-29, 2016</em>, pages 3338--3345,
  2016.

</li>

<li><a name="Wilson16"></a>

A.&nbsp;G. Wilson, Z.&nbsp;Hu, R.&nbsp;R. Salakhutdinov, and E.&nbsp;P. Xing.
 Stochastic Variational Deep Kernel Learning.
 In D.&nbsp;D. Lee, M.&nbsp;Sugiyama, U.&nbsp;V. Luxburg, I.&nbsp;Guyon, and R.&nbsp;Garnett,
  editors, <em>Advances in Neural Information Processing Systems 29</em>, pages
  2586--2594. Curran Associates, Inc., 2016.

</li></ul>
### Bayesian Convolutional Nets
<ul>



<li><a name="Tran19"></a>

G.-L. Tran, E.&nbsp;V. Bonilla, J.&nbsp;Cunningham, P.&nbsp;Michiardi, and M.&nbsp;Filippone.
 Calibrating Deep Convolutional Gaussian Processes.
 In K.&nbsp;Chaudhuri and M.&nbsp;Sugiyama, editors, <em>Proceedings of Machine
  Learning Research</em>, volume&nbsp;89 of <em>Proceedings of Machine Learning
  Research</em>, pages 1554--1563. PMLR, 16--18 Apr 2019.

</li>

<li><a name="Laumann18"></a>

F.&nbsp;Laumann, K.&nbsp;Shridhar, and A.&nbsp;L. Maurin.
 Bayesian Convolutional Neural Networks, June 2018.
 arXiv:1806.05978.

</li>

<li><a name="Garriga-Alonso18"></a>

A.&nbsp;Garriga-Alonso, C.&nbsp;E. Rasmussen, and L.&nbsp;Aitchison.
 Deep Convolutional Networks as shallow Gaussian Processes.
 In <em>International Conference on Learning Representations</em>, 2019.

</li>

<li><a name="Gal16b"></a>

Y.&nbsp;Gal and Z.&nbsp;Ghahramani.
 Bayesian convolutional neural networks with Bernoulli approximate
  variational inference.
 In <em>4th International Conference on Learning Representations
  (ICLR) workshop track</em>, 2016.

</li></ul>
### Calibration of Bayesian Convolutional Nets
<ul>



<li><a name="Tran19"></a>

G.-L. Tran, E.&nbsp;V. Bonilla, J.&nbsp;Cunningham, P.&nbsp;Michiardi, and M.&nbsp;Filippone.
 Calibrating Deep Convolutional Gaussian Processes.
 In K.&nbsp;Chaudhuri and M.&nbsp;Sugiyama, editors, <em>Proceedings of Machine
  Learning Research</em>, volume&nbsp;89 of <em>Proceedings of Machine Learning
  Research</em>, pages 1554--1563. PMLR, 16--18 Apr 2019.

</li>

<li><a name="Lakshminarayanan17"></a>

B.&nbsp;Lakshminarayanan, A.&nbsp;Pritzel, and C.&nbsp;Blundell.
 Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles.
 In I.&nbsp;Guyon, U.&nbsp;V. Luxburg, S.&nbsp;Bengio, H.&nbsp;Wallach, R.&nbsp;Fergus,
  S.&nbsp;Vishwanathan, and R.&nbsp;Garnett, editors, <em>Advances in Neural Information
  Processing Systems 30</em>, pages 6402--6413. Curran Associates, Inc., 2017.

</li>

<li><a name="Niculescu-Mizil05"></a>

A.&nbsp;Niculescu-Mizil and R.&nbsp;Caruana.
 Predicting Good Probabilities with Supervised Learning.
 In <em>Proceedings of the 22Nd International Conference on Machine
  Learning</em>, ICML '05, pages 625--632, New York, NY, USA, 2005. ACM.

</li>

<li><a name="Guo17"></a>

C.&nbsp;Guo, G.&nbsp;Pleiss, Y.&nbsp;Sun, and K.&nbsp;Q. Weinberger.
 On Calibration of Modern Neural Networks.
 In D.&nbsp;Precup and Y.&nbsp;W. Teh, editors, <em>Proceedings of the 34th
  International Conference on Machine Learning</em>, volume&nbsp;70 of <em>Proceedings
  of Machine Learning Research</em>, pages 1321--1330, International Convention
  Centre, Sydney, Australia, Aug. 2017. PMLR.

</li></ul>
### Random Feature Expansions for Shallow and Deep Gaussian Processes
<ul>



<li><a name="Cutajar17"></a>

K.&nbsp;Cutajar, E.&nbsp;V. Bonilla, P.&nbsp;Michiardi, and M.&nbsp;Filippone.
 Random feature expansions for deep Gaussian processes.
 In D.&nbsp;Precup and Y.&nbsp;W. Teh, editors, <em>Proceedings of the 34th
  International Conference on Machine Learning</em>, volume&nbsp;70 of <em>Proceedings
  of Machine Learning Research</em>, pages 884--893, International Convention
  Centre, Sydney, Australia, Aug. 2017. PMLR.

</li>

<li><a name="Yu16"></a>

F.&nbsp;X. Yu, A.&nbsp;T. Suresh, K.&nbsp;M. Choromanski, D.&nbsp;N. Holtmann-Rice, and S.&nbsp;Kumar.
 Orthogonal Random Features.
 In D.&nbsp;D. Lee, M.&nbsp;Sugiyama, U.&nbsp;V. Luxburg, I.&nbsp;Guyon, and R.&nbsp;Garnett,
  editors, <em>Advances in Neural Information Processing Systems 29</em>, pages
  1975--1983. Curran Associates, Inc., 2016.

</li>

<li><a name="Le13"></a>

Q.&nbsp;Le, T.&nbsp;Sarlos, and A.&nbsp;Smola.
 Fastfood - Approximating Kernel Expansions in Loglinear Time.
 In <em>30th International Conference on Machine Learning (ICML)</em>,
  2013.

</li>

<li><a name="Gal16"></a>

Y.&nbsp;Gal and Z.&nbsp;Ghahramani.
 Dropout As a Bayesian Approximation: Representing Model Uncertainty
  in Deep Learning.
 In <em>Proceedings of the 33rd International Conference on
  International Conference on Machine Learning - Volume 48</em>, ICML'16, pages
  1050--1059. JMLR.org, 2016.

</li>

<li><a name="Rahimi08"></a>

A.&nbsp;Rahimi and B.&nbsp;Recht.
 Random Features for Large-Scale Kernel Machines.
 In J.&nbsp;C. Platt, D.&nbsp;Koller, Y.&nbsp;Singer, and S.&nbsp;T. Roweis, editors, <em>
  Advances in Neural Information Processing Systems 20</em>, pages 1177--1184.
  Curran Associates, Inc., 2008.

</li></ul>
### Variational Inference
<ul>



<li><a name="Kingma14"></a>

D.&nbsp;P. Kingma and M.&nbsp;Welling.
 Auto-Encoding Variational Bayes.
 In <em>Proceedings of the Second International Conference on
  Learning Representations (ICLR 2014)</em>, Apr. 2014.

</li>

<li><a name="Graves11"></a>

A.&nbsp;Graves.
 Practical Variational Inference for Neural Networks.
 In J.&nbsp;Shawe-Taylor, R.&nbsp;S. Zemel, P.&nbsp;L. Bartlett, F.&nbsp;Pereira, and
  K.&nbsp;Q. Weinberger, editors, <em>Advances in Neural Information Processing
  Systems 24</em>, pages 2348--2356. Curran Associates, Inc., 2011.

</li></ul>
### Variational Inference for Gaussian Process Models
<ul>



<li><a name="nguyen-bonilla-nips-2014"></a>

T.&nbsp;V. Nguyen and E.&nbsp;V. Bonilla.
 Automated variational inference for Gaussian process models.
 In <em>Advances in Neural Information Processing Systems</em>. 2014.

</li>

<li><a name="hensman-uai-13"></a>

J.&nbsp;Hensman, N.&nbsp;Fusi, and N.&nbsp;D. Lawrence.
 Gaussian processes for big data.
 In <em>Uncertainty in Artificial Intelligence</em>, 2013.

</li>

<li><a name="dezfouli-bonilla-nips-2015"></a>

A.&nbsp;Dezfouli and E.&nbsp;V. Bonilla.
 Scalable inference for Gaussian process models with black-box
  likelihoods.
 In <em>Advances in Neural Information Processing Systems</em>. 2015.

</li>

<li><a name="bonilla-arxiv-2016"></a>

E.&nbsp;V. Bonilla, K.&nbsp;Krauth, and A.&nbsp;Dezfouli.
 Generic Inference in Latent Gaussian Process Models.
 <em>arXiv e-prints</em>, page arXiv:1609.00577, Sep 2016.

</li>

<li><a name="titsias-aistats-09"></a>

M.&nbsp;Titsias.
 Variational learning of inducing variables in sparse Gaussian
  processes.
 In <em>Artificial Intelligence and Statistics</em>, 2009.

</li>

<li><a name="krauth-et-al-uai-2017"></a>

K.&nbsp;Krauth, E.&nbsp;V. Bonilla, K.&nbsp;Cutajar, and M.&nbsp;Filippone.
 AutoGP: Exploring the capabilities and limitations of Gaussian
  process models.
 In <em>Uncertainty in Artificial Intelligence</em>, 2017.

</li></ul>
### Unsupervised learning with Deep Gaussian Processes
<ul>



<li><a name="Domingues18"></a>

R.&nbsp;Domingues, P.&nbsp;Michiardi, J.&nbsp;Zouaoui, and M.&nbsp;Filippone.
 Deep Gaussian process autoencoders for novelty detection.
 <em>Machine Learning</em>, 107(8-10):1363--1383, 2018.

</li>

<li><a name="Dai15"></a>

Z.&nbsp;Dai, A.&nbsp;Damianou, J.&nbsp;Gonz&aacute;lez, and N.&nbsp;Lawrence.
 Variational Auto-encoded Deep Gaussian Processes, Feb. 2016.

</li>

<li><a name="lawrence-jmlr-2005"></a>

N.&nbsp;Lawrence.
 Probabilistic non-linear principal component analysis with Gaussian
  process latent variable models.
 <em>Journal of Machine Learning Research</em>, 6:1783--1816, 2005.

</li>

<li><a name="Grochow:2004"></a>

K.&nbsp;Grochow, S.&nbsp;L. Martin, A.&nbsp;Hertzmann, and Z.&nbsp;Popovi&#x107;.
 Style-based inverse kinematics.
 In <em>ACM SIGGRAPH 2004 Papers</em>, SIGGRAPH '04, pages 522--531, New
  York, NY, USA, 2004. ACM.

</li></ul>
### Multi-task Learning with Gaussian Processes
<ul>



<li><a name="chai2008multi"></a>

K.&nbsp;M.&nbsp;A. Chai, C.&nbsp;K. Williams, S.&nbsp;Klanke, and S.&nbsp;Vijayakumar.
 Multi-task Gaussian process learning of robot inverse dynamics.
 In <em>Advances in Neural Processing Systems</em>, pages 265--272, 2008.

</li>

<li><a name="bonilla-et-al-nips-08"></a>

E.&nbsp;V. Bonilla, K.&nbsp;M.&nbsp;A. Chai, and C.&nbsp;K.&nbsp;I. Williams.
 Multi-task Gaussian process prediction.
 In <em>Advances in Neural Processing Systems</em>, 2008.

</li>

<li><a name="alvarez2011computationally"></a>

M.&nbsp;A. &Aacute;lvarez and N.&nbsp;D. Lawrence.
 Computationally efficient convolved multiple output Gaussian
  processes.
 <em>Journal of Machine Learning Research</em>, 12(5):1459--1500, 2011.

</li>

<li><a name="wilson-et-al-icml-12"></a>

A.&nbsp;G. Wilson, D.&nbsp;A. Knowles, and Z.&nbsp;Ghahramani.
 Gaussian process regression networks.
 In <em>International Conference on Machine Learning</em>, 2012.

</li>

<li><a name="boyle2007gaussian"></a>

P.&nbsp;Boyle.
 <em>Gaussian Processes for Regression and Optimisation</em>.
 PhD thesis, Victoria University of Wellington, 2007.

</li></ul>
### Bayesian Optimization
<ul>



<li><a name="Jones2001"></a>

D.&nbsp;R. Jones.
 A Taxonomy of Global Optimization Methods Based on Response
  Surfaces.
 <em>Journal of Global Optimization</em>, 21(4):345--383, 2001.

</li>

<li><a name="snoek2012practical"></a>

J.&nbsp;Snoek, H.&nbsp;Larochelle, and R.&nbsp;P. Adams.
 Practical Bayesian optimization of machine learning algorithms.
 In <em>Advances in neural information processing systems</em>, pages
  2951--2959, 2012.

</li>

<li><a name="chu2005preference"></a>

W.&nbsp;Chu and Z.&nbsp;Ghahramani.
 Preference learning with Gaussian processes.
 In <em>Proceedings of the 22nd international conference on Machine
  learning</em>, pages 137--144. ACM, 2005.

</li>

<li><a name="swersky2013multi"></a>

K.&nbsp;Swersky, J.&nbsp;Snoek, and R.&nbsp;P. Adams.
 Multi-task Bayesian optimization.
 In <em>Advances in neural information processing systems</em>, pages
  2004--2012, 2013.

</li></ul>
### Other GP and DGP Models
<ul>



<li><a name="Domingues18"></a>

R.&nbsp;Domingues, P.&nbsp;Michiardi, J.&nbsp;Zouaoui, and M.&nbsp;Filippone.
 Deep Gaussian process autoencoders for novelty detection.
 <em>Machine Learning</em>, 107(8-10):1363--1383, 2018.

</li>

<li><a name="VanDerWilk17"></a>

M.&nbsp;van&nbsp;der Wilk, C.&nbsp;E. Rasmussen, and J.&nbsp;Hensman.
 Convolutional Gaussian Processes.
 In I.&nbsp;Guyon, U.&nbsp;V. Luxburg, S.&nbsp;Bengio, H.&nbsp;Wallach, R.&nbsp;Fergus,
  S.&nbsp;Vishwanathan, and R.&nbsp;Garnett, editors, <em>Advances in Neural Information
  Processing Systems 30</em>, pages 2849--2858. Curran Associates, Inc., 2017.

</li>

<li><a name="LorenziNeuroImage17"></a>

M.&nbsp;Lorenzi, M.&nbsp;Filippone, G.&nbsp;B. Frisoni, D.&nbsp;C. Alexander, and S.&nbsp;Ourselin.
 Probabilistic disease progression modeling to characterize diagnostic
  uncertainty: staging and prediction in Alzheimer's disease.
 <em>NeuroImage</em>, 2017.
 to appear.

</li>

<li><a name="Dai15"></a>

Z.&nbsp;Dai, A.&nbsp;Damianou, J.&nbsp;Gonz&aacute;lez, and N.&nbsp;Lawrence.
 Variational Auto-encoded Deep Gaussian Processes, Feb. 2016.

</li>

<li><a name="blomqvist2018deep"></a>

K.&nbsp;Blomqvist, S.&nbsp;Kaski, and M.&nbsp;Heinonen.
 Deep convolutional Gaussian processes.
 <em>arXiv preprint arXiv:1810.03052</em>, 2018.

</li>

<li><a name="pmlr-v54-galliani17a"></a>

P.&nbsp;Galliani, A.&nbsp;Dezfouli, E.&nbsp;Bonilla, and N.&nbsp;Quadrianto.
 Gray-box inference for structured Gaussian process models.
 In A.&nbsp;Singh and J.&nbsp;Zhu, editors, <em>Proceedings of the 20th
  International Conference on Artificial Intelligence and Statistics</em>,
  volume&nbsp;54 of <em>Proceedings of Machine Learning Research</em>, pages 353--361,
  Fort Lauderdale, FL, USA, 20--22 Apr 2017. PMLR.

</li>

<li><a name="linderman2014discovering"></a>

S.&nbsp;Linderman and R.&nbsp;Adams.
 Discovering latent network structure in point process data.
 In <em>International Conference on Machine Learning</em>, pages
  1413--1421, 2014.

</li>

<li><a name="pmlr-v80-dezfouli18a"></a>

A.&nbsp;Dezfouli, E.&nbsp;Bonilla, and R.&nbsp;Nock.
 Variational network inference: Strong and stable with concrete
  support.
 In J.&nbsp;Dy and A.&nbsp;Krause, editors, <em>Proceedings of the 35th
  International Conference on Machine Learning</em>, volume&nbsp;80 of <em>Proceedings
  of Machine Learning Research</em>, pages 1204--1213, Stockholmsmässan, Stockholm
  Sweden, 10--15 Jul 2018. PMLR.

</li>

<li><a name="kuss2004gaussian"></a>

M.&nbsp;Kuss and C.&nbsp;E. Rasmussen.
 Gaussian processes in reinforcement learning.
 In <em>Advances in neural information processing systems</em>, pages
  751--758, 2004.

</li>

<li><a name="engel2005reinforcement"></a>

Y.&nbsp;Engel, S.&nbsp;Mannor, and R.&nbsp;Meir.
 Reinforcement learning with Gaussian processes.
 In <em>Proceedings of the 22nd international conference on Machine
  learning</em>, pages 201--208. ACM, 2005.

</li>

<li><a name="deisenroth2011pilco"></a>

M.&nbsp;Deisenroth and C.&nbsp;E. Rasmussen.
 Pilco: A model-based and data-efficient approach to policy search.
 In <em>Proceedings of the 28th International Conference on machine
  learning (ICML-11)</em>, pages 465--472, 2011.

</li>

<li><a name="martin2018recursive"></a>

J.&nbsp;Martin and B.&nbsp;Englot.
 Recursive sparse pseudo-input Gaussian process sarsa.
 <em>arXiv preprint arXiv:1811.07201</em>, 2018.

</li>

<li><a name="adams2009tractable"></a>

R.&nbsp;P. Adams, I.&nbsp;Murray, and D.&nbsp;J. MacKay.
 Tractable nonparametric Bayesian inference in poisson processes
  with Gaussian process intensities.
 In <em>Proceedings of the 26th Annual International Conference on
  Machine Learning</em>, pages 9--16. ACM, 2009.

</li>

<li><a name="lloyd-icml-2015"></a>

C.&nbsp;Lloyd, T.&nbsp;Gunter, M.&nbsp;A. Osborne, and S.&nbsp;J. Roberts.
 Variational Inference for Gaussian Process Modulated Poisson
  Processes.
 In <em>International Conference on Machine Learning</em>, 2015.

</li>

<li><a name="john2018large"></a>

S.&nbsp;John and J.&nbsp;Hensman.
 Large-scale cox process inference using variational fourier features.
 <em>International Conference on Machine Learning</em>, 2018.

</li>

<li><a name="aglietti2018efficient"></a>

V.&nbsp;Aglietti, T.&nbsp;Damoulas, and E.&nbsp;Bonilla.
 Efficient inference in multi-task cox process models.
 <em>Artificial Intelligence and Statistics</em>, 2018.

</li></ul>