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<h1>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">#</a></h1>
<div class="docutils container" id="id1">
<dl class="citation">
<dt class="label" id="id14"><span class="brackets">ABvdW21</span></dt>
<dd><p>Artem Artemev, David R. Burt, and Mark van der Wilk. Tighter bounds on the log marginal likelihood of gaussian process regression using conjugate gradients. In <em>Proceedings of the 38th International Conference on Machine Learning</em>, 362–372. 2021.</p>
</dd>
<dt class="label" id="id11"><span class="brackets">CS09</span></dt>
<dd><p>Youngmin Cho and Lawrence K. Saul. Kernel methods for deep learning. In <em>Advances in Neural Information Processing Systems 22</em>. 2009. URL: <a class="reference external" href="http://papers.nips.cc/paper/3628-kernel-methods-for-deep-learning.pdf">http://papers.nips.cc/paper/3628-kernel-methods-for-deep-learning.pdf</a>.</p>
</dd>
<dt class="label" id="id2"><span class="brackets">CG05</span></dt>
<dd><p>Wei Chu and Zoubin Ghahramani. Gaussian processes for ordinal regression. <em>Journal of Machine Learning Research</em>, 6(Jul):1019–1041, 2005.</p>
</dd>
<dt class="label" id="id6"><span class="brackets">HMFG15</span></dt>
<dd><p>James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, and Zoubin Ghahramani. Mcmc for variatinoally sparse gaussian processes. In <em>Proceedings of NIPS</em>. 2015. URL: <a class="reference external" href="https://proceedings.neurips.cc/paper/2015/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf">https://proceedings.neurips.cc/paper/2015/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf</a>.</p>
</dd>
<dt class="label" id="id5"><span class="brackets">HMG15</span></dt>
<dd><p>James Hensman, Alexander G. de G. Matthews, and Zoubin Ghahramani. Scalable variational gaussian process classification. In <em>Proceedings of AISTATS</em>. 2015.</p>
</dd>
<dt class="label" id="id12"><span class="brackets">LazaroGFV09</span></dt>
<dd><p>Miguel Lázaro-Gredilla and An\'ıbal Figueiras-Vidal. Inter-domain gaussian processes for sparse inference using inducing features. In <em>Advances in Neural Information Processing Systems 22</em>. 2009.</p>
</dd>
<dt class="label" id="id7"><span class="brackets">Law03</span></dt>
<dd><p>Neil Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. <em>Advances in neural information processing systems</em>, 2003.</p>
</dd>
<dt class="label" id="id8"><span class="brackets">Llo14</span></dt>
<dd><p>James Robert et al Lloyd. Automatic construction and natural-language description of nonparametric regression models. In <em>Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence</em>. 2014. URL: <a class="reference external" href="http://dl.acm.org/citation.cfm?id=2893873.2894066">http://dl.acm.org/citation.cfm?id=2893873.2894066</a>.</p>
</dd>
<dt class="label" id="id9"><span class="brackets">MHTG16</span></dt>
<dd><p>Alexander G de G Matthews, James Hensman, Richard Turner, and Zoubin Ghahramani. On sparse variational methods and the kullback-leibler divergence between stochastic processes. In <em>Artificial Intelligence and Statistics</em>, 231–239. PMLR, 2016.</p>
</dd>
<dt class="label" id="id3"><span class="brackets">MvandWilkN+17</span></dt>
<dd><p>Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman. GPflow: A Gaussian process library using TensorFlow. <em>Journal of Machine Learning Research</em>, 18(40):1–6, apr 2017. URL: <a class="reference external" href="http://jmlr.org/papers/v18/16-537.html">http://jmlr.org/papers/v18/16-537.html</a>.</p>
</dd>
<dt class="label" id="id10"><span class="brackets">Mat17</span></dt>
<dd><p>Alexander Graeme de Garis Matthews. <em>Scalable Gaussian process inference using variational methods</em>. PhD thesis, University of Cambridge, 2017.</p>
</dd>
<dt class="label" id="id13"><span class="brackets">OA09</span></dt>
<dd><p>Manfred Opper and Cedric Archambeau. The variational gaussian approximation revisited. <em>Neural Comput.</em>, pages 786–792, 2009.</p>
</dd>
<dt class="label" id="id15"><span class="brackets">SEH18</span></dt>
<dd><p>Hugh Salimbeni, Stefanos Eleftheriadis, and James Hensman. Natural gradients in practice: non-conjugate variational inference in gaussian process models. In <em>AISTATS</em>. 2018.</p>
</dd>
<dt class="label" id="id16"><span class="brackets">SG06</span></dt>
<dd><p>Edward Snelson and Zoubin Ghahramani. Sparse gaussian processes using pseudo-inputs. In <em>Advances In Neural Information Processing Systems</em>, 1257–1264. MIT press, 2006.</p>
</dd>
<dt class="label" id="id18"><span class="brackets">TL10</span></dt>
<dd><p>Michalis Titsias and Neil D Lawrence. Bayesian gaussian process latent variable model. In <em>Proceedings of the thirteenth international conference on artificial intelligence and statistics</em>, 844–851. JMLR Workshop and Conference Proceedings, 2010.</p>
</dd>
<dt class="label" id="id17"><span class="brackets">Tit09</span></dt>
<dd><p>Michalis K Titsias. Variational learning of inducing variables in sparse gaussian processes. In <em>International Conference on Artificial Intelligence and Statistics</em>, 567–574. 2009.</p>
</dd>
<dt class="label" id="id19"><span class="brackets">Tit14</span></dt>
<dd><p>Michalis K. Titsias. Variational inference for gaussian and determinantal point processes. Dec 2014. URL: <a class="reference external" href="http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf">http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf</a>.</p>
</dd>
<dt class="label" id="id20"><span class="brackets">vdWRH17</span></dt>
<dd><p>Mark van der Wilk, Carl Edward Rasmussen, and James Hensman. Convolutional gaussian processes. In <em>Advances in Neural Information Processing Systems 30</em>. 2017. URL: <a class="reference external" href="http://papers.nips.cc/paper/6877-convolutional-gaussian-processes.pdf">http://papers.nips.cc/paper/6877-convolutional-gaussian-processes.pdf</a>.</p>
</dd>
<dt class="label" id="id4"><span class="brackets">vandWilkDJ+20</span></dt>
<dd><p>Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, and James Hensman. A framework for interdomain and multioutput Gaussian processes. <em>arXiv:2003.01115</em>, 2020. URL: <a class="reference external" href="https://arxiv.org/abs/2003.01115">https://arxiv.org/abs/2003.01115</a>.</p>
</dd>
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