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<h5>Description:</h5>
<p>Multi-objective hyperparameter optimization with performance uncertainty. Combining Gaussian Process Regression and Tree Parzen Estimators.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/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:inneke.vannieuwenhuyse@uhasselt.be" target="_blank" rel="noopener">Inneke Van Nieuwenhuyse</a></li>
<li><strong>Authors</strong>: <a href="mailto:inneke.vannieuwenhuyse@uhasselt.be" target="_blank" rel="noopener">Inneke Van Nieuwenhuyse</a>, <a href="mailto:alejandro.moraleshernandez@uhasselt.be" target="_blank" rel="noopener">Alejandro Morales Hern&aacute;ndez</a></li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://github.com/BayesOpt-UHasselt/GP_TPE" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories:</strong> Optimization</li>
</ul>