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<h5>Description:</h5>
<p>A Tensorflow library (successor to <a href="GPFlowOpt" target="_blank" rel="noopener">GPFlowOpt</a>) 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/onderzoek/onderzoeksuitdagingen/ai-driven-data-science" data-entry-id="58" target="_blank" rel="noopener" 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:Jixiang.qing@ugent.be" target="_blank" rel="noopener">Jixiang Qi,</a> <a href="mailto:nasrulloh.loka@ugent.be" target="_blank" rel="noopener">Nasrulloh Loka</a>, <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://secondmind-labs.github.io/trieste/" target="_blank" rel="noopener">Link to the website</a></li>
<li><a href="https://github.com/secondmind-labs/trieste" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories</strong>: Optimization, Active learning</li>
</ul>