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<h5>Description:</h5>
<p>A Tensorflow 1 library (See Trieste for the version for the latest Tensorflow library) with the latest Bayesian optimization strategies. Bayesian optimization can be used for efficient hyperparameter tuning (AutoML), smart labeling of expensive data, data-efficient engineering design, and much more.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/en/research/research-challenges-2019-2023/ai-driven-data-science"[entry:airesearch:58:/onderzoek/onderzoeksuitdagingen/ai-driven-data-science]data-entry-id="58">GC1</a> - WP4</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:Tom.dhaene@ugent.be" target="_blank" rel="noopener">Tom Dhaene</a></li>
<li><strong>Authors</strong>: <a href="mailto:Ivo.couckuyt@ugent.be" target="_blank" rel="noopener">Ivo Couckuyt</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://gpflowopt.readthedocs.io/en/latest/" target="_blank" rel="noopener">Link to the website</a></li>
<li><a href="https://github.com/GPflow/GPflowOpt" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Optimization, Active learning</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>N. Knudde, J. van der Herten, T. Dhaene, I. Couckuyt, "<a href="https://arxiv.org/abs/1711.03845" target="_blank" rel="noopener">GPflowOpt: A Bayesian Optimization Library using TensorFlow</a>", &nbsp;arXiv:1711.03845v1 [stat.ML] , 2017</li>
</ul>

<p>Library for Bayesian optimization and active learning (only for Tensorflow 1.x)</p>